Drone device security system for protecting a package

ABSTRACT

A fact checking system utilizes social networking information and analyzes and determines the factual accuracy of information and/or characterizes the information by comparing the information with source information. The social networking fact checking system automatically monitors information, processes the information, fact checks the information and/or provides a status of the information, including automatically modifying a web page to include the fact check results. The fact checking system is able to be implemented utilizing a drone device. The drone device is able to be implemented in conjunction with a security system.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application is a continuation application of co-pending U.S. patentapplication Ser. No. 16/695,947, filed on Nov. 26, 2019, and titled“DRONE DEVICE SECURITY SYSTEM FOR PROTECTING A PACKAGE,” which is acontinuation application of co-pending U.S. patent application Ser. No.16/169,328 (now U.S. Pat. No. 10,538,329), filed on Oct. 24, 2018, andtitled “DRONE DEVICE SECURITY SYSTEM FOR PROTECTING A PACKAGE,” which isa continuation application of co-pending U.S. patent application Ser.No. 15/628,907 (now U.S. Pat. No. 10,183,748), filed on Jun. 21, 2017,and titled “DRONE DEVICE SECURITY SYSTEM FOR PROTECTING A PACKAGE,”which is a continuation-in-part application of U.S. patent applicationSer. No. 15/472,858 (now U.S. Pat. No. 10,035,594), filed on Mar. 29,2017, and titled “DRONE DEVICE SECURITY SYSTEM,” which is a continuationapplication of U.S. patent application Ser. No. 15/422,642 (now U.S.Pat. No. 9,643,722), filed on Feb. 2, 2017, and titled “DRONE DEVICESECURITY SYSTEM,” which is a continuation-in-part application of U.S.patent application Ser. No. 14/729,223 (now U.S. Pat. No. 9,892,109),filed on Jun. 3, 2015, and titled “AUTOMATICALLY CODING FACT CHECKRESULTS IN A WEB PAGE,” which is a continuation-in-part application ofU.S. patent application Ser. No. 14/260,492 (now U.S. Pat. No.9,972,055), filed on Apr. 24, 2014, and titled “FACT CHECKING METHOD ANDSYSTEM UTILIZING SOCIAL NETWORKING INFORMATION,” which claims thebenefit of U.S. Provisional Patent Application Ser. No. 61/946,043,filed Feb. 28, 2014, and titled “FACT CHECKING METHOD AND SYSTEMUTILIZING SOCIAL NETWORKING INFORMATION,” which are all herebyincorporated by reference in their entireties for all purposes. U.S.patent application Ser. No. 15/422,642, filed on Feb. 2, 2017, andtitled “DRONE DEVICE SECURITY SYSTEM,” also claims the benefit of U.S.Provisional Patent Application Ser. No. 62/403,594, filed Oct. 3, 2016,and titled “FACT CHECKING USING A DRONE DEVICE, which is herebyincorporated by reference in its entirety for all purposes.

FIELD OF THE INVENTION

The present invention relates to the field of social networking systemsanalysis.

BACKGROUND OF THE INVENTION

Information is easily dispersed through social networks and socialmedia. The accuracy of the information is often questionable or evenincorrect. Although there are many fact checkers, they typically sufferfrom speed, efficiency, accuracy and other issues.

SUMMARY OF THE INVENTION

A social networking fact checking system analyzes and determines thefactual accuracy of information and/or characterizes the information bycomparing the information with source information. The social networkingfact checking system automatically monitors information, processes theinformation, fact checks the information and/or provides a status of theinformation, including automatically modifying a web page to include thefact check results.

The social networking fact checking system provides users with factuallyaccurate information, limits the spread of misleading or incorrectinformation, provides additional revenue streams, and supports manyother advantages.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a flowchart of a method of implementing fact checkingaccording to some embodiments.

FIG. 2 illustrates a block diagram of an exemplary computing deviceconfigured to implement the fact checking method according to someembodiments.

FIG. 3 illustrates a network of devices configured to implement factchecking according to some embodiments.

FIG. 4 illustrates a flowchart of a method of implementing social factchecking according to some embodiments.

FIG. 5 illustrates a flowchart of a method of utilizing social networkcontacts for fact checking according to some embodiments.

FIG. 6 illustrates a flowchart of a method of fact checking a user forregistration according to some embodiments.

FIG. 7 illustrates a flowchart of a method of determining a validityrating based on contacts' information according to some embodiments.

FIG. 8 illustrates an exemplary web of lies according to someembodiments.

FIG. 9 illustrates an exemplary web of lies in timeline format accordingto some embodiments.

FIG. 10 illustrates a flowchart of a method of affecting a user based ona validity rating according to some embodiments.

FIG. 11 illustrates a flowchart of a method of connecting users based onsimilar content or validity rating according to some embodiments.

FIG. 12 illustrates a flowchart of a method of fact checking mappinginformation.

FIG. 13 illustrates a flowchart of a method of using an icon to indicatea validity rating or the validity of information provided by an entityaccording to some embodiments.

FIG. 14 illustrates a flowchart of a method of awarding honors for factchecking according to some embodiments.

FIG. 15 illustrates a flowchart of a method of touchscreen fact checkingaccording to some embodiments.

FIG. 16 illustrates a flowchart of a method of automatically coding aweb page with fact check results according to some embodiments.

FIG. 17 illustrates a flowchart of microblogging with fact checkingaccording to some embodiments.

FIG. 18 illustrates a flowchart of a method of fact checking utilizingfact checking analytics according to some embodiments.

FIG. 19 illustrates a diagram of an exemplary drone for fact checkingand content generation according to some embodiments.

FIG. 20 illustrates a diagram of multiple drones according to someembodiments.

FIG. 21 illustrates a diagram of a nested drone implementation accordingto some embodiments.

FIG. 22 illustrates a diagram of the primary drone and the secondarydrone of the nested drone separated.

FIG. 23 illustrates a flowchart of a method of fact checking informationusing drone information according to some embodiments.

FIG. 24 illustrates a flowchart of a method of utilizing a drone tocapture and provide information according to some embodiments.

FIG. 25 illustrates a diagram of a drone security system according tosome embodiments.

FIG. 26 illustrates a diagram of a drone with shielding according tosome embodiments.

FIG. 27 illustrates a flowchart of a method of utilizing the droneand/or security system to prevent an object from being removed from alocation according to some embodiments.

FIG. 28 illustrates a diagram of devices for securing an object at alocation according to some embodiments.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

A fact checking system utilizing social networking informationdetermines the factual accuracy of information by comparing theinformation with source information. Additional analysis is able to beimplemented as well such as characterizing the information.

FIG. 1 illustrates a flowchart of a method of implementing fact checkingaccording to some embodiments.

In the step 100, information is monitored. In some embodiments, allinformation or only some information (e.g., a subset less than all ofthe information) is monitored. In some embodiments, only explicitlyselected information is monitored. In some embodiments, although allinformation is monitored, only some information (e.g., informationdeemed to be fact-based) is fact checked.

The information includes, but is not limited to, broadcast information(e.g., television broadcast information, radio broadcast information),email, documents, database information, social networking/media content(tweets/Twitter®, Facebook® postings), webpages, message boards, weblogs, any computing device communication, telephonecalls/communications, audio, text, live speeches/audio, radio,television video/text/audio, VoIP calls, video chatting, videoconferencing, images, videos, and/or any other information. Theinformation is able to be in the form of phrases, segments, sentences,numbers, words, comments, values, graphics, and/or any other form.

In some embodiments, monitoring includes recording, scanning, capturing,transmitting, tracking, collecting, surveying, and/or any other type ofmonitoring. In some embodiments, monitoring includes determining if aportion of the information is able to be fact checked. For example, ifinformation has a specified structure, then it is able to be factchecked.

In some embodiments, the social networking fact checking system isimplemented without monitoring information. This is able to beimplemented in any manner. For example, while information is transmittedfrom a source, the information is also processed and fact checked sothat the fact check result is able to be presented. In some embodiments,the fact check result is embedded in the same stream as the information.In some embodiments, the fact check result is in the header of a packet.

In the step 102, the information is processed. Processing is able toinclude many aspects including, but not limited to, converting (e.g.,audio into text), formatting, parsing, determining context,transmitting, converting an image into text, analyzing andreconfiguring, and/or any other aspect that enables the information tobe fact checked. Parsing, for example, includes separating a long speechinto separate phrases that are each separately fact checked. Forexample, a speech may include 100 different facts that should beseparately fact checked. In some embodiments, the step 102 is able to beskipped if processing is not necessary (e.g., text may not need to beprocessed). In some embodiments, processing includes converting theinformation into a searchable format. In some embodiments, processingoccurs concurrently with monitoring. In some embodiments, processingincludes capturing/receiving and/or transmitting the information (e.g.,to/from the cloud).

In a specific example of processing, information is converted intosearchable information (e.g., audio is converted into searchable text),and then the searchable information is parsed into fact checkableportions (e.g., segments of the searchable text; several word phrases).

Parsing is able to be implemented in any manner including, but notlimited to, based on sentence structure (e.g., subject/verbdetermination), based on punctuation including, but not limited to, endpunctuation of each sentence (e.g., period, question mark, exclamationpoint), intermediate punctuation such as commas and semi-colons, basedon other grammatical features such as conjunctions, based on capitalletters, based on a duration of a pause between words (e.g., 2 seconds),based on duration of a pause between words by comparison (e.g., typicalpauses between words for user are 0.25 seconds and pauses betweenthoughts are 1 second)—the user's speech is able to be analyzed todetermine speech patterns such as length of pauses between words lastinga fourth of the length for pauses between thoughts or sentences, basedon a change of a speaker (e.g., speaker A is talking, then speaker Bstarts talking), based on a word count (e.g., 10 word segments), basedon speech analysis, based on a slowed down version (recording thecontent, slowing down the recorded content to determine timing breaks),based on keywords/key phrases, based on search results, and/or any othermanner. In some embodiments, processing includes, but is not limited to,calculating, computing, storing, recognition, speaker recognition,language (word, phrase, sentence, other) recognition, labeling, and/orcharacterizing.

In the step 104, the information is fact checked. Fact checking includescomparing the information to source information to determine the factualvalidity, accuracy, quality, character and/or type of the information.In some embodiments, the source information includes web pages on theInternet, one or more databases, dictionaries, encyclopedias, socialnetwork information, video, audio, any other communication, any otherdata, one or more data stores and/or any other source.

In some embodiments, the comparison is a text comparison such as astraight word for word text comparison. In some embodiments, thecomparison is a context/contextual comparison. In some embodiments, anatural language comparison is used. In some embodiments, patternmatching is utilized. In some embodiments, an intelligent comparison isimplemented to perform the fact check. In some embodiments, exact match,pattern matching, natural language, intelligence, context, and/or anycombination thereof is used for the comparison. Any method of analyzingthe source information and/or comparing the information to the sourceinformation to analyze and/or characterizing the information is able tobe implemented. An exemplary implementation of fact checking includessearching (e.g., a search engine's search), parsing the results orsearching through the results of the search, comparing the results withthe information to be checked using one or more of the comparisons(e.g., straight text, context or intelligent) and retrieving resultsbased on the comparison (e.g., if a match is found return “True”). Theresults are able to be any type including, but not limited to, binary,Boolean (True/False), text, numerical, and/or any other format. In someembodiments, determining context and/or other aspects of convertingcould be implemented in the step 104. In some embodiments, the sourcesare rated and/or weighted. For example, sources are able to be givenmore weight based on accuracy of the source, type of the source, userpreference, user selections, classification of the source, and/or anyother weighting factor. The weighting is then able to be used indetermining the fact check result. For example, if a highly weighted orrated source agrees with a comment, and a low weighted source disagreeswith the comment, the higher weighted source is used, and “valid” or asimilar result is returned. Determining a source agrees with informationis able to be implemented in any manner, for example, by comparing theinformation with the source and finding a matching result, anddetermining a source disagrees with information is when the comparisonof the information and the source does not find a match.

In the step 106, a status of the information is provided based on thefact check result. The status is provided in any manner including, butnot limited to, transmitting and/or displaying text, highlighting,underlining, color effects, a visual or audible alert or alarm, agraphical representation, and/or any other indication. The meaning ofthe status is able to be any meaning including, but not limited to,correct, incorrect, valid, true, false, invalid, opinion, hyperbole,sarcasm, hypocritical, comedy, unknown, questionable, suspicious, needmore information, questionable, misleading, deceptive, possibly, closeto the truth, and/or any other status.

The status is able to be presented in any manner, including, but notlimited to, lights, audio/sounds, highlighting, text, a text bubble, ascrolling text, color gradient, headnotes/footnotes, an iconic orgraphical representation, a video or video clip, music, other visual oraudio indicators, a projection, a hologram, a tactile indicatorincluding, but not limited to, vibrations, an olfactory indicator, aTweet, a text message (SMS, MMS), an email, a page, a phone call, asocial networking page/transmission/post/content, or any combinationthereof. For example, text is able to be highlighted or the text coloris able to change based on the validity of the text. For example, as auser types a social network message, the true statements are displayedin green, the questionable statements are displayed in yellow, and thefalse statements are displayed in red. In some embodiments, providingthe status includes transmitting and/or broadcasting the status to oneor more devices (e.g., televisions).

The status is also able to include other information including, but notlimited to, statistics, citations and/or quotes. Providing the status ofthe information is also able to include providing additional informationrelated to the fact checked information, such as an advertisement. Insome embodiments, providing includes pointing out, showing, displaying,recommending, playing, presenting, announcing, arguing, convincing,signaling, asserting, persuading, demonstrating, denoting, expressing,hinting, illustrating, implying, tagging, labeling, characterizing,and/or revealing.

In some embodiments, the fact checking system is implemented such thatresponses, validity determinations and/or status presentations areavailable in real-time or near real-time. By real-time, it is meantinstantaneously (e.g., within 1 second); whereas near real-time iswithin a few seconds (e.g., within 5 seconds). Furthermore, since themonitoring, processing, fact checking and providing status are all ableto be performed automatically without user intervention, real-time alsomeans faster than having a human perform the search and presentingresults. Depending on the implementation, in some embodiments, theindication is presented in at most 1 second, at most several seconds(e.g., at most 5 seconds), at most a minute (not real-time), at mostseveral minutes or by the end of a show. In some embodiments, the timeamount (e.g., at most 1 second) begins once a user pauses in typing,once a phrase has been communicated, once a phrase has been determined,at the end of a sentence, once an item is flagged, or another point in asequence. For example, as soon as a phrase is detected, the factchecking system checks the fact, returns a result and displays anindication based on the result in less than 1 second—clearly much fasterthan a human performing a search, analyzing the search results and thentyping a result to be displayed on a screen.

In some embodiments, an indication is displayed to compare the factcheck result with other fact check results for other users. For example,as described herein, in some embodiments, fact check implementations areable to be different for different users based on selections such asapprovals of sources and processing selections which are able to resultin different fact check results. Therefore, if User A is informed that Xinformation is determined to be “false,” an indication indicates that Xinformation was determined to be “true” for 50 other people. In someembodiments, usernames are indicated (e.g., X information was determinedto be “true” for Bob). In some embodiments, usernames and/or results areonly provided if their result is different from the user's result. Insome embodiments, the number of users whose result matches the user'sresult is indicated. In some embodiments, the indication only indicateswhat the results were for contacts (e.g., social networking contacts) ofthe user. In some embodiments, the indication is only indicated if theresults were different (e.g., true for user, but false for others). Insome embodiments, the indication includes numbers or percentages ofother fact check implementations (e.g., true for 50 users and false for500 users or 25% true and 75% false). In some embodiments, indicationsare only indicated for specific users or classes of users. For example,only results of users classified as “members of the media” areindicated. In another example, a user is able to select whose resultsare indicated. In some embodiments, only results of users with avalidity rating above a threshold are indicated.

In some embodiments, fewer or more steps are implemented. Furthermore,in some embodiments, the order of the steps is modified. In someembodiments, the steps are performed on the same device, and in someembodiments, one or more of the steps, or parts of the steps, areseparately performed and/or performed on separate devices. In someembodiments, each of the steps 100, 102, 104 and 106 occur or are ableto occur in real-time or non-real-time. Any combination of real-time andnon-real-time steps is possible such as all real-time, none real-timeand everything in between.

FIG. 2 illustrates a block diagram of an exemplary computing device 200configured to implement the fact checking method according to someembodiments. The computing device 200 is able to be used to acquire,store, compute, process, communicate and/or display informationincluding, but not limited to, text, images, videos and audio. In someexamples, the computing device 200 is able to be used to monitorinformation, process the information, fact check the information and/orprovide a status of the information. In general, a hardware structuresuitable for implementing the computing device 200 includes a networkinterface 202, a memory 204, a processor 206, I/O device(s) 208, a bus210 and a storage device 212. The choice of processor is not critical aslong as a suitable processor with sufficient speed is chosen. The memory204 is able to be any conventional computer memory known in the art. Thestorage device 212 is able to include a hard drive, CDROM, CDRW, DVD,DVDRW, flash memory card, solid state drive or any other storage device.The computing device 200 is able to include one or more networkinterfaces 202. An example of a network interface includes a networkcard connected to an Ethernet or other type of LAN. The I/O device(s)208 are able to include one or more of the following: keyboard, mouse,monitor, display, printer, modem, touchscreen, touchpad,speaker/microphone, voice input device, button interface, hand-waving,body-motion capture, touchless 3D input, joystick, remote control,brain-computer interface/direct neural interface/brain-machineinterface, camera, and other devices. In some embodiments, the hardwarestructure includes multiple processors and other hardware to performparallel processing. Fact checking application(s) 230 used to performthe monitoring, processing, fact checking and providing are likely to bestored in the storage device 212 and memory 204 and processed asapplications are typically processed. More or fewer components shown inFIG. 2 are able to be included in the computing device 200. In someembodiments, fact checking hardware 220 is included. Although thecomputing device 200 in FIG. 2 includes applications 230 and hardware220 for implementing the fact checking, the fact checking method is ableto be implemented on a computing device in hardware, firmware, softwareor any combination thereof. For example, in some embodiments, the factchecking applications 230 are programmed in a memory and executed usinga processor. In another example, in some embodiments, the fact checkinghardware 220 is programmed hardware logic including gates specificallydesigned to implement the method.

In some embodiments, the fact checking application(s) 230 includeseveral applications and/or modules. Modules include a monitoring modulefor monitoring information, a processing module for processing (e.g.,converting) information, a fact checking module for fact checkinginformation and a providing module for providing a status of theinformation. In some embodiments, modules include one or moresub-modules as well. In some embodiments, fewer or additional modulesare able to be included. In some embodiments, the applications and/orthe modules are located on different devices. For example, a deviceperforms monitoring, processing, and fact checking, but the providing isperformed on a different device, or in another example, the monitoringand processing occurs on a first device, the fact checking occurs on asecond device and the providing occurs on a third device. Anyconfiguration of where the applications/modules are located is able tobe implemented such that the fact checking system is executed.

Examples of suitable computing devices include, but are not limited to apersonal computer, a laptop computer, a computer workstation, a server,a mainframe computer, a handheld computer, a personal digital assistant,a pager, a telephone, a fax machine, a cellular/mobile telephone, asmart appliance, a gaming console, a digital camera, a digitalcamcorder, a camera phone, a smart phone/device (e.g., a Droid® or aniPhone®), a portable music player (e.g., an iPod®), a tablet (e.g., aniPad®), a video player, an e-reader (e.g., Kindle™), a DVDwriter/player, an HD (e.g., Blu-ray®) or ultra high densitywriter/player, a television, a copy machine, a scanner, a car stereo, astereo, a satellite, a DVR (e.g., TiVo®), a smart watch/jewelry, smartdevices, a home entertainment system or any other suitable computingdevice.

FIG. 3 illustrates a network of devices configured to implement factchecking according to some embodiments. The network of devices 300 isable to include any number of devices and any various devices including,but not limited to, a computing device (e.g., a tablet) 302, atelevision 304, a smart device 306 (e.g., a smart phone) and a source308 (e.g., a database) coupled through a network 310 (e.g., theInternet). The source device 308 is able to be any device containingsource information including, but not limited to, a searchable database,web pages, transcripts, statistics, historical information, or any otherinformation or device that provides information. The network 310 is ableto any network or networks including, but not limited to, the Internet,an intranet, a LAN/WAN/MAN, wireless, wired, Ethernet, satellite, acombination of networks, or any other implementation of communicating.The devices are able to communicate with each other through the network310 or directly to each other. One or more of the devices is able to bean end user device, a media organization, a company and/or anotherentity. In some embodiments, peer-to-peer sourcing is implemented. Forexample, the source of the data to be compared with is not on acentralized source but is found on peer sources.

Social

In some embodiments, social fact checking is implemented. In someembodiments, only a user's content and/or sources and/or a user'scontacts' content and/or sources are used for fact checking. The sourceinformation is able to be limited in any manner such as by generating adatabase and filling the database only with information found in theuser's contacts' content/sources. In some embodiments, the social factchecking only utilizes content that the user has access to, such thatcontent and/or sources of users of a social networking system who arenot contacts of the user are not accessible by the user and are not usedby the fact checking system. In some embodiments, source information islimited to social networking information such that the social networkinginformation is defined as content generated by or for, stored by or for,or controlled by or for a specified social networking entity (e.g.,Facebook®, Twitter®, LinkedIn®). The social network entity is able to berecognized by a reference to the entity being stored in a datastructure. For example, a database stores the names of social networkingentities, and the database is able to be referenced to determine if asource is a social networking source or not. In some embodiments, sourceinformation is limited to the social networking information that hasbeen shared by a large number of users (e.g., over 1,000) or a verylarge number of users (e.g., over 1,000,000). The social networkinginformation is able to be shared in any manner such as sharedpeer-to-peer, shared directly or indirectly between users, shared bysending a communication directly or indirectly via a social networkingsystem. For example, source information is limited to only tweets, onlytweets received by at least 100 users, only Facebook® postings that areviewed by at least 100 users, only Facebook® postings of users with 100or more contacts, only users who are “followed” by 100 or more users,and/or any other limitation or combination of limitations. In someembodiments, source information is acquired by monitoring a system suchas Twitter®. For example, microblogs (e.g., tweets) are monitored, andin real-time or non-real-time, the tweets are analyzed and incorporatedas source information. Furthering the example, the tweets are processed(e.g., parsed), fact checked and/or compared with other information, andresults and/or other information regarding the tweets are stored assource information. In some embodiments, the source information islimited to social networking information and additional sourceinformation. For example, source information is limited to socialnetworking information and other sources with a reliability rating above9 (on scale of 1 to 10). In another example, source information islimited to social networking information and specific sources such asencyclopedias and dictionaries. In some embodiments, the fact checkoccurs while the user is logged into the social networking system anduses the content accessible at that time. In some embodiments, if acontact is invited but has not accepted, his content/sources are stillused. In some embodiments, contacts are able to be separated intodifferent groups such as employers, employees or by position/level(e.g., partners and associates), and the different groups are able to beused for fact checking. In some embodiments, only a user's friends'content and/or sources are used for fact checking. In some embodiments,multiple fact checks are implemented based on the groups (e.g., one factchecker including friends' information and a second fact checkerincluding co-workers' information). In some embodiments, fact checkresults are sent to contacts (e.g., social network contacts) of a user.In some embodiments, fact check results are shared using socialnetworking. For example, a user fact checks an advertisement or theadvertisement is fact checked for a user, and the result is sent toand/or displayed for contacts in the social network. In someembodiments, users are able to select if they want to receive fact checkresults from contacts. In some embodiments, users are able to be limitedto contacts where they only receive fact check results but do not haveother access (e.g., no access to personal information). For example, auser watches a show which is fact checked. When misinformation isdetected, the fact check result is sent to the user and his contacts(e.g., via a tweet). In some embodiments, only certain types of factcheck results are sent to users (e.g., only lies and misinformation).The misinformation and lies are able to be determined in any manner. Forexample, misinformation is determined automatically by determining thefactual accuracy of the information, and if the information isdetermined to be factually inaccurate, then it is misinformation. Liesare able to be determined by determining information is misinformationand analyzing intent. Intent is able to be analyzed in any manner, forexample, context (e.g., additional information, a result) of a statementis analyzed to determine intent. Misinformation, lies and othercharacterizations are able to be determined using a look-up table whichclassifies and stores information as factually accurate, misinformation,lies, and/or other characterizations. In some embodiments, informationis distinguished as misinformation or lies by manual review and/orcrowdsourcing. For example, users are presented a comment includingcontext (e.g., a video clip), and the users indicate if they think it ismisinformation or an intentional lie, then based on the user selections,a result is indicated. In some embodiments, additional information issent with the result to provide context such as a clip of the originalcontent which had the misinformation or a link to the original content.In some embodiments, social information is stored/utilized in anefficient manner. For example, personal data is stored in a fastestaccess memory location, and non-personal information provided by a useron a social network is stored in a slower location. In some embodiments,information is further prioritized based on popularity, relevance, time(recent/old), and/or any other implementation.

FIG. 4 illustrates a flowchart of a method of implementing social factchecking according to some embodiments. In the step 400, information isanalyzed. Analyzing is able to include monitoring, processing, and/orother forms of analysis. In the step 402, automatic fact checking isperformed utilizing only social network information as sourceinformation. In some embodiments, the social network information is onlysocial network information from contacts of the user (or contacts ofcontacts). In some embodiments, social network information is notlimited to contacts of the user. In some embodiments, if the automaticfact checking fails to produce a result above a quality/confidencethreshold, then manual crowdsourcing fact checking is implemented togenerate a result, in the step 404. The manual crowdsourcing isimplemented by providing/sending the information to be fact checkedwhere many users are able to find the information, fact check theinformation and send a response which is used to generate a factchecking result. For example, 1000 users perform manual crowdsourcingfact checking, and 995 of the users send a response indicating theinformation is false, and the fact checking system generates a factcheck result that the information is false. The fact check result isable to be determined in any way, for example, majority rules, percentabove/below a threshold or any other way. In the step 406, the result ispresented on the user's device. In some embodiments, fewer or additionalsteps are implemented. In some embodiments, automatic fact checking andcrowdsourcing are performed in parallel. In some embodiments, anautomatic result and crowdsource result are compared, and the resultwith a higher confidence score is used. In some embodiments, bothresults including the confidence score of each are provided. Confidenceof a result is able to be determined in any manner; for example, basedon how close source information is to the information, based on thenumber of agreeing/disagreeing sources, and/or any other manner. Forexample, if 99 sources agree with a statement (e.g., have the same textas the statement) and only 1 source disagrees with the statement (e.g.,has text that indicates or means the opposite of the statement), thenthe confidence score is 99%.

In some embodiments, only sources that a user and/or a user's contacts(e.g., social network contacts) have approved/accepted/selected are usedfor fact checking. Users are able to approve/accept sources in anymanner, such as clicking approve after visiting a website, or fillingout a survey, or not clicking disapprove after visiting a website wherethe site is automatically approved by visiting, approving via socialnetworking (e.g., receiving a link or site or content from a contact),by “liking” content, by sending a tweet with a hashtag or othercommunication with the source to approve, by selecting content (e.g.,from list of selectable sources), using another social media forum(e.g., items/photos pinned on Pinterest are approved by that user,videos liked on Youtube are approved by those users) or any otherimplementation. In some embodiments, a source is approved if the sourceis fact checked by the user. In some embodiments, a source is approvedif the source has been fact checked by another entity (e.g.,automatically by fact checking system), and the user has verified oraccepted the fact check results. In some embodiments, a user is able todesignate an entity which approves/disapproves of sources for the user.For example, the user selects an automated approval/disapproval systemwhich searches/crawls sources (e.g., databases, the Web), analyzes(e.g., parses) the sources, fact checks the sources, and based on theanalysis and/or fact check results, approves/disapproves of the sourcesfor the user. In some embodiments, a source is approved if the source isassociated with an organization/entity that the user has “liked” (or asimilar implementation), where associated means approved by, written by,affiliated with, or another similar meaning In some embodiments, a siteor other source information becomes an approved source if a user uses orvisits the source. In some embodiments, a source is approved if a useruses or visits the source while signed/logged in (e.g., signed in toFacebook® or Google+®). In some embodiments, the user must be loggedinto a specific social networking system, and in some embodiments, theuser is able to be logged into any social networking system or aspecific set of social networking systems. In some embodiments, thesources are limited to a specific method of approval such as onlysources visited while logged in. In some embodiments, a source isapproved if the source is recommended to the user (e.g., by a contact)(even if the user does not visit/review the source), unless or until theuser rejects/disapproves of the source. In some embodiments, sources aresuggested to a user for a user to accept or reject based on contacts ofthe user and/or characteristics of the user (e.g., location, politicalaffiliation, job, salary, organizations, recently watched programs,sites visited). In some embodiments, the contacts are limited to n-levelcontacts (e.g., friends of friends but not friends of friends offriends). In an example, user A approved source X, and one of hiscontacts approved source Y, and another contact approved source Z. Soonly sources X, Y and Z are used for fact checking content for user A.Furthering the example, since user A's sources may be different thanuser J's sources, it is possible to have different fact checking resultsfor different users. In some embodiments, users are able to disapprovesources. In some embodiments, if there is a conflict (e.g., one userapproves of a source and a contact disapproves of the same source), thenthe choice of the user with a higher validity rating is used. In someembodiments, if there is a conflict, the selection of the contact withthe closer relationship to the user (the user being interpreted as theclosest contact) is used. In some embodiments, if there is a conflictand multiple users approve/disapprove, then the higher of the number ofapprovals versus disapprovals determines the result (e.g., 2 usersapprove Site X and 5 users disapprove Site X, then Site X is not used).In another example, if 50 contacts approve Web Page Y, and 10 contactsdisapprove Web Page Y, then Web Page Y is approved. Again, depending onthe contacts, the fact check results could be different for differentusers. Furthering the example, User A has 50 contacts that approve WebPage Y, and 10 that disapprove. However, User B has 5 contacts thatapprove Web Page Y, and 20 contacts that disapprove Web Page Y.Therefore, Web Page Y is approved for User A and disapproved for User B.In some embodiments, users are able to approve/disapprove sources inclusters, and users are able to cluster sources. In some embodiments,users are able to share/recommend sources to contacts (e.g., via asocial networking site). For example, user A says, “I've grouped thesesources; I think they are all legit,” and the contacts are able toaccept or reject some/all of the sources. In some embodiments, togenerate viral approvals/disapprovals, when a user approves ordisapproves of a source or a group of sources, the source (or referencesto the source, for example, a link to the source) and the approval ordisapproval are automatically sent to contacts of the user (or up to nthlevel contacts of the user, for example, contacts of contacts of theuser). Similarly, when contacts of a user approve/disapprove a source,the source or reference and approval/disapproval are automatically sentto the user. In some embodiments, when a user approves/disapproves of asource, the source is automatically approved/disapproved for contacts.In some embodiments, the contacts are able to modify theapproval/disapproval; for example, although the user approved a source,Contact X selects to disapprove the source, so it is not an approvedsource for Contact X. Similarly, when contacts approve/disapprove asource, the source is automatically approved/disapproved for the userunless the user modifies the approval/disapproval. In some embodiments,users are able to limit the automatic approval to nth level contacts(e.g., only 1st and 2nd level contacts but no higher level contacts). Insome embodiments, all sources or a subset of sources (e.g., all sourcesincluding social networking content generated by users while logged intoa social networking site) are approved until a user disapproves of asource (or group of sources), and then that source (or group of sources)is disapproved. In some embodiments, sources are approved based on atweet and a hashtag. For example, a user tweets a message with the nameof a source preceded by a hashtag symbol. In another example, a usertweets a message with a link to a source or a name of a source and“#fcapproval” or “#fcdisapproval” or similar terms to approve/disapprovea source. In some embodiments, sources are approved based on contentwatched (e.g., on television, YouTube), items purchased, stores/sitesshopped at, and/or other activities by the user. For example, the userwatches Program X which uses/approves sources A, B and C foranalyzing/determining content, so those sources automatically becomeapproved for the user. In some embodiments, the sources are approvedonly if the user “likes” the show or if it is determined the userwatches the show long enough and/or enough times. For example, a countertracks how long the user watches a show, and if/when the counter goesabove a threshold, the sources affiliated with/related to the show areautomatically approved. In some embodiments, sources are linked so thatif a user approves a source, any source that is linked to the source isalso approved automatically. In some embodiments, the linked sources arerequired to be related (e.g., same/similar genre, same/similarreliability rating). For example, a user approves Dictionary A which islinked to Dictionary B and Dictionary C, so Dictionaries A, B and C, allbecome approved when the user approves Dictionary A. In someembodiments, the linked sources are displayed for the user toselect/de-select (e.g., in a pop-up window). In some embodiments,approval/disapproval of sources is transmitted via color-coded ornon-color-coded messages such as tweets, text messages and/or emails. Insome embodiments, approvals/disapprovals are transmitted automaticallyto contacts of the user upon approval/disapproval. In some embodiments,when a user is about to approve/disapprove a source, an indication ofwhat others (e.g., contacts or non-contacts of the user) have selectedis presented. For example, the user visits Webpage Z, and in the bottomcorner of the browser (or elsewhere), it is displayed that Contact Jdisapproved this source. In some embodiments, all sources are acceptedexcept ones the user manually rejects. In some embodiments, sources areable to be selected by sensing a user circling/selecting icons or otherrepresentations of the sources. In some embodiments, sources areapproved by bending a flexible screen when the source is on the screen.For example, a bend is detected by detecting pressure in the screen orin another manner, and the device determines the current source beingdisplayed. In some embodiments, sources are selected based on an entity.For example, a user specifies to use Fox News's content and sources asapproved sources. In some embodiments, any content/sources that Fox Newsdisapproved is also recognized as disapproved for the user. In someembodiments, users are able to combine entities and their sources; forexample, a user selects to use Fox News content/sources and CNNcontent/sources. If there are any conflicts, such as Fox News approvingSource X and CNN disapproving Source X, the conflicts are able to behandled in any manner such as those described herein. In someembodiments, the user handles the conflicts by selectingapprove/disapprove of each conflicting item or selects a preferredentity (e.g., if conflict, prefer CNN, so CNN's selections are chosen).In some embodiments, sources are received from/by others, and thesources are filtered based on personal preferences, characteristics,and/or selections such that only sources satisfying preferences areaccepted automatically and others are rejected or placed on hold forapproval. For example, User A is a very liberal person as determinedbased on viewing and reading habits, so when User G sends three sourcesthat he thinks User A should approve, two of the three sources areclassified as liberal, so they are automatically approved for User A,and the third source is classified as conservative, so it is placed in aqueue for User A to review and approve/disapprove. In some embodiments,sources are approved by detecting a user in a specified location. Forexample, the device determines that it is at or is detected at apolitical rally. The content/sources of the politician holding the rallyare automatically approved for the user or are presented for the userfor manual approval. In some embodiments, content/sources of theopponent of the politician are automatically disapproved (unless theyhad previously been approved; for example, by detecting them as alreadyapproved by the user). In some embodiments, when a device determinesthat it is within range of another user (e.g., by facial recognition) oranother user's device (e.g., by detecting device ID or user ID), theapproved/disapproved sources and their approval/disapproval status isprovided on the device. In some embodiments, user's are able to limittheir approval/disapproval information (e.g., only contacts are able toview). In some embodiments, sources are approved by waving a device at asource. For example, RFID or another technology is used to determinewhat sources are in close proximity (e.g., user waves smart phone in alibrary, and the books become approved sources for the user). In someembodiments, in addition to or instead of accepting/rejecting sources,users are able to set/select any other option regarding fact checkingimplementations such as which content to monitor, keywords formonitoring, how content is processed, weighting schemes for sources,priorities, and/or any other fact checking implementation option.

In some embodiments, a source is approved based on the reliabilityrating of the source and the approvals/disapprovals of the source. Forexample, a source is approved if the reliability rating and theapprovals total above a threshold. In another example, a reliabilityrating is increased by 1 (or another number) if the number of approvalsis greater than the number of disapprovals, and the reliability ratingis decreased by 1 (or another number) if the number of approvals is notgreater than the number of disapprovals, and then the source is approvedif the modified reliability rating is above a threshold. In anotherexample, the reliability rating is added to the number of approvalsdivided by the number of disapprovals divided by ten or the number ofapprovals plus the number of disapprovals, and then the modifiedreliability rating is compared with a threshold, and if the modifiedreliability rating is above the threshold, then the source is approved.In another example, the reliability rating is multiplied by the numberof approvals divided by the number of disapprovals with a cap/maximumtotal (e.g., 10), and then the modified reliability rating is comparedwith a threshold, and if the modified reliability rating is above thethreshold, then the source is approved. Any calculation is able to beimplemented to utilize the reliability rating, approvals anddisapprovals to determine if a source is approved for fact checking. Insome embodiments, weights are added to the calculations; for example, auser's approval/disapproval is given extra weight. For example,reliability rating+user's approval/disapproval (+2/−2)+contacts'approvals/disapprovals (+1/−1).

In some embodiments, social networking teams/groups are able to be setup for fact checking such that each member of a team approves,disapproves, and/or rates sources for fact checking. In someembodiments, each member of a team rates/selects other options regardingfact checking as well such as monitoring criteria, processing criteriaand/or other criteria, and the selections are used to determine how tofact check information. For example, three members of a team select toparse after every pause in the monitored information of two seconds, andtwo members select to parse after every 10 seconds, so the selection ofafter every pause of two seconds is used. In some embodiments, socialnetwork groups' fact checking results are compared to determine the mostaccurate group. For example, Groups A, B and C are compared, and thegroup with the most correct results is considered to be the mostaccurate group. Furthering the example, a set of data is fact checkedusing Group A's sources, Group B's sources, and Group C's sources, andthen the fact checking results are analyzed automatically, manually orboth to determine the most accurate fact checking results. Furtheringthe example, Group A's results were 80% accurate, Group B's results were95% accurate, and Group C's results were 50% accurate, so Group B wasthe most accurate. The groups' results are able to be comparedautomatically, manually or both. For example, if groups' results matchsimilar to the automatic fact checking system results, the groups'results are determined to be accurate. In another example, a group'sresults are analyzed manually (e.g., by a group of impartialindividuals) and manually compared with an automated fact checkingsystem's results or other groups' results. Furthering the example, thesources selected/approved by a group are used to automatically factcheck content, and the results of those fact checks are manually orautomatically compared with automatic fact check implementations usingdifferent sources or other groups' implementations. In some embodiments,the groups are ranked by accuracy. In some embodiments, the mostaccurate groups' sources (e.g., top 10 groups) are made public and/orselectable by other users, and/or the most accurate groups' sources aresent via social media (e.g., tweeted or posted) to other users with anoption to accept/reject.

Rating sources includes providing a reliability rating of a source, avalidity rating of a source, fact checking a source, and/or any otherrating. For example, a user of a team rates an opinion blog as a 1 outof 10 (1 meaning very factually inaccurate), and then the opinion blogis fact checked utilizing an automatic fact checking system (ormanually) which determines the content of the opinion blog is mostlyfactually inaccurate, so the automatic fact checking system gives arating of 1 as well. In some embodiments, users of teams do not specifya rating number for a source; rather, the users of the teamsapprove/disapprove/select sources, and the team with the most accuratesources (e.g., in number and/or in accuracy) is considered to be themost accurate team. In some embodiments, “accurate” such as an accuratesource is defined as having a reliability or accuracy rating above athreshold (e.g., above 8 on a scale of 1 to 10 with 10 being the mostaccurate), and the reliability/accuracy rating is able to be based onhow accurate the information is; for example, the information is factchecked (automatically and/or manually) and based on the fact check, thereliability/accuracy rating is determined. Furthering the example, ifthe fact check returns “factually accurate” for all segments ofinformation, then the information receives a 10 for accuracy, and if thefact check returns “factually inaccurate” for all segments of theinformation, then the information receives a 0 for accuracy. In someembodiments, sources are manually analyzed to determine areliability/accuracy rating. In an example of teams with the mostaccurate sources, a team with 1 source that is fact checked by a factchecking system and determined to be a reliability rating of 10, isconsidered to be less accurate than a team with 10 sources that all havea reliability rating of 10. In other words, accuracy and breadth of thesources are taken into account to determine the team with the bestsources. In some embodiments, the sources are classified, and breadth isdetermined not just by quantity of sources but also by the numberclasses the sources fall into. For example, 100 sources in a singleclassification (e.g., sports history) are not as accurate as 100 sourcesin 10 classifications. In some embodiments, the opposite is true. Forexample, a large number sources in a single classification would ensurea fact check using those sources would be accurate, and a sourcecollection that is very broad would not necessarily help. Furthering theexample, if the fact checking system is fact checking the first team towin back to back Super Bowls, a set of sources which include a medicalencyclopedia and a french dictionary would not be better than a set ofsources that focuses on sports. In some embodiments, accuracy is givenmore weight, and in some embodiments, breadth is given more weight. Forexample, in some embodiments, a set of 100 sources with an averagereliability rating of 9 is better than a set of 1000 sources with anaverage reliability rating of 8. In some embodiments, the set of 1000sources is considered better even though the reliability rating isslightly lower, since more information may be able to be fact checkedwith the larger breadth. In some embodiments, both sets are availableand used for fact checking, and whichever one returns with a higherconfidence score is used to provide a status of the information beingfact checked.

FIG. 5 illustrates a flowchart of a method of utilizing social networkcontacts for fact checking according to some embodiments. In the step500, users approve or disapprove sources or otherfeatures/options/elements for fact checking. In the step 502, factchecking is implemented (e.g., monitoring, processing, fact checkingand/or providing a result). In some embodiments, additional or fewersteps are implemented.

In some embodiments, sources are weighted based on the number of usersthat have accepted/rejected them. For example, a dictionary that has1000 accepts and 0 rejects is rated higher than a biased site which has5 accepts and 900 rejects. In some embodiments, this is a factor used inconjunction with other weighting systems. For example, a fact check isperformed on sources to generate a reliability rating, and theaccept/reject weighting is an additional factor for determining a finalreliability rating (e.g., reliability rating+/−accept/rejectweighting=final reliability rating).

In some embodiments, users in a social network are grouped/havedifferent levels (e.g., media, business level, regular user, politician)which affects the weight of the sources. For example, a media levelsource is given a higher weight than a regular user source.

In some embodiments, the weight of the source is utilized in factchecking such that higher weighted sources have more influence on a factcheck result. For example, a calculation in determining a fact checkresult includes: determining the number of agreeing highest weightedsources which is multiplied by the highest weight value, determining thenumber of agreeing second highest weighted sources which is multipliedby the second highest weight value, and so on until determining thenumber of agreeing lowest weighted sources which is multiplied by thelowest weight value. Then, the results are combined to determine a totalvalue, and if the total value is above a threshold, then the informationbeing fact checked is determined “confirmed,” and if the total value isnot above the threshold, then the information is “unconfirmed” or“disproved.” In another, example, the weights are applied to disagreeingsources, and if the total value is above a threshold, then theinformation is “disproved,” or if the total value is not above thethreshold, then the information is “confirmed.” In yet another example,the weighted agreeing and disagreeing values are combined or subtracted,and if the result is above a threshold, then “confirmed” and if not,then “disproved.”

In some embodiments, users' sources are weighted based on “tokens” oruser validity ratings (e.g., the higher the validity rating or highernumber of tokens earned, then the higher the source weight).

In some embodiments, emails or other messages are sent to contacts withfact check result updates. For example, an email is automatically sentwhen a contact's validity rating drops below a threshold. Other forms ofcommunication are possible such as a tweet, text message, or instantmessage.

In some embodiments, people are fact checked to confirm they are whothey say they are. For example, when a person registers for a socialnetworking site, the person is verified by fact checking. A user is ableto be verified in any manner, such as: comparing the user informationwith another social networking site, comparing the user information withan online resume, using IP address location information, using pasthistory information, comparing a past photograph of the user with acurrent photograph or a video scan (e.g., using a webcam), analyzingschool information, analyzing work information, analyzing professionalorganization information, and/or analyzing housing information.

In some embodiments, when users attempt to connect (e.g., when a userasks to join a user/friend's network or when a user is asked to joinanother user's (e.g., friend) network), a question is asked. Forexample, the user asks the friend a question, and the user determines ifthe answer is correct or not, which determines if the friend is acceptedinto the network or not. In another example, the friend asks the user aquestion, and the friend determines if the answer is correct or not,which determines if the user is accepted into the network or not. Insome embodiments, for efficiency, the user asks a generic/broad questionthat is able to be applied to many users, so the user does not have togenerate specific questions for each user. For example, “what highschool did we go to?”. In some embodiments, when a user makes aninvitation to a second user, the user inputs a question for the seconduser to answer. In some embodiments, instead of or in addition to a userasking a question, the second user (or invitee) simply sends a personalmessage that informs the user that the second user is, in fact, who hesays he is. For example, the invitee accepts the invitation, and alsomakes a comment, “I remember that weird painting of the dog in your dormroom at Stanford.” Then, the user either accepts or rejects the seconduser. In some embodiments, a user is allowed to “connect” to anotheruser but with limited access until he is verified.

FIG. 6 illustrates a flowchart of a method of fact checking a user forregistration according to some embodiments. In the step 600, a userattempts to register (e.g., with a social networking site/system or asecond social networking site/system). In the step 602, the user isverified using fact checking. In the step 604, the user attempts toconnect with a friend. In the step 606, user/friend verification occurs.In some embodiments, fewer or additional steps are implemented.

In some embodiments, after a web page, tweet, and/or any other contentis fact checked, the fact check result and any identifying information(e.g., the parsed segment) is stored and used as source information, orstored in a manner that is easily retrievable for future displays ofresults. In some embodiments, the results are stored in a cache or otherquickly accessible location. In some embodiments, the results are storedin a script (e.g., javascript) with the web page or coded in the webpage, or another implementation.

Validity Rating and Web

In some embodiments, an entity including, but not limited to, a speaker,author, user, or another entity (e.g., corporation) has a validityrating that is included with the distribution of information fromhim/it. The validity rating is able to be based on fact checking resultsof comments made by an entity or any other information. For example, ifa person has a web page, and 100% of the web page is factually accurate,then the user is given a 10 (on a scale of 1 to 10) for a validityrating. In another example, a user tweets often, and half of the tweetsare factually accurate and half are inaccurate, the user is given a 5.The validity rating is able to be calculated in any manner. In additionto fact checking information by an entity, items such as controversies,bias, and/or any other relevant information is able to be used incalculating a validity rating. The severity of the information ormisinformation is also able to be factored in when rating a person orentity. Additionally, the subject of the information or misinformationis also able to be taken into account in terms of severity. In someembodiments, an independent agency calculates a validity rating and/ordetermines what is major and what is minor. In some embodiments,individual users are able to indicate what is important to them and whatis not. In some embodiments, another implementation of determining whatis major, minor and in between is implemented. The context of thesituation/statement is also able to be taken into account. In someembodiments, entities are able to improve their validity rating if theyapologize for or correct a mistake, although measures are able to betaken to prevent abuses of apologies. In some embodiments, in additionto or instead of a validity rating, an entity is able to include anotherrating, including, but not limited to, a comedic rating or a politicalrating. In some embodiments, an entity includes a classificationincluding, but not limited to, political, comedy or opinion. Examples ofinformation or statistics presented when an entity appears include, butare not limited to the number of lies, misstatements, truthfulstatements, hypocritical statements or actions, questionable statements,spin, and/or any other characterizations.

FIG. 7 illustrates a flowchart of a method of determining a validityrating based on contacts' information according to some embodiments. Inthe step 700, a user's validity rating is determined or acquired. In thestep 702, the user's contacts' validity ratings are determined oracquired. In the step 704, a complete user's validity rating isdetermined based on the user's validity rating and the contacts'validity ratings. In some embodiments, additional or fewer steps areimplemented and/or the order of the steps is modified. For example, thesteps are continuously ongoing such that if anything changes in eitherthe user's validity rating or the contacts' validity ratings, then newratings, including a new complete validity rating, are computed.

In some embodiments, relationship information is utilized in factchecking. For example, if a user's contacts have low entity/validityratings, then that information negatively affects the user's entityrating. For example, a user's base validity rating is a 7 out of 10based on fact checking results of the user's comments. Based on socialnetworking relationships, the user has 4 friends/contacts with 1 degreeof separation from the user, and each of those friends has a 2 out of 10validity rating. If the user's validity rating is calculated as FinalValidity Rating=(Base Validity Rating*10+Average Friend ValidityRating*5)/15, then the Final Validity Rating=(7*10+2*5)/15=5.3. Inanother example, the user's validity rating is calculated as FinalValidity Rating=(Base Validity Rating*10+Average Friend ValidityRating*# of friends)/(# of friends+10), then the Final ValidityRating=(7*10+2*4)/(4+10)=5.6. In some embodiments, contacts withadditional degrees of separation are utilized in determining the user'svalidity rating. In some embodiments, the additional degrees ofseparation are weighted less, and the weighting decreases as the degreeof separation increases. For example, a user's validity rating is 7, 4friends have validity ratings of 2, and 2 friends of friends havevalidity ratings of 6. If the user's validity rating is calculated asFinal Validity Rating=(Base Validity Rating*10+Average Friend ValidityRating*5+Average Second Degree Friend Validity Rating*2)/17, then theFinal Validity Rating=(7*10+2*5+6*2)/17=5.4.

In some embodiments, a web of lies/misinformation/other characterizationis generated. A web is able to be generated by fact checking informationand determining the relationship of who said what and when. Onceinformation is determined to be misleading, analysis is performed todetermine who provided the information, and then analysis is determinedif anyone provided the information before, and relationships aredetermined based on the time/date of the information and/or if there isany connection between those providing the information. For example, theweb of misinformation includes a graphic of who spreads misinformation.Each point in the web is able to be an entity or information. Forexample, a set of Republicans who made the same lie are included in thering with the misinformation shown in the middle. In another example,the web is a timeline version where the web shows who first said thelie, and then who repeated it. In some embodiments, times/dates of whenthe misinformation was said or passed on is indicated. In someembodiments, the first person to say the lie is given more negativeweight (e.g., for validity rating) as they are the origin of the lie. Inanother example, a tree structure is used to display the connections oflies. Although specific examples have been provided, there are manydifferent ways of storing the information and showing who provided theinformation. The web is able to be displayed for viewers to see who saysthe same information or agrees with a person. The web is shown when themisinformation is detected or when one of the people in the web isdetected. For example, commentator X provided misinformation, and 5people also provided the same misinformation. When commentator X isdetected (e.g., voice or facial recognition), a graphic is presentedshowing the 5 additional people who provided the same misinformation ascommentator X.

FIG. 8 illustrates an exemplary web of lies according to someembodiments. In the web shown, a first level provider 800 ofmisinformation is shown in the middle of the web. Second level 802 andthird level 804 misinformation providers are also shown further out inthe web.

FIG. 9 illustrates an exemplary web of lies in timeline format accordingto some embodiments. In the timeline format of the web, a first provider900 of misinformation is shown, followed by a second provider 902, thirdprovider 904, fourth provider 906, and fifth provider 908.

The web is also able to be used to generate relationships betweenentities. For example, user A says, “global warming is a hoax.” Then,users who have made a similar or the same comment (e.g., on theirFacebook® page, personal website, on a message board, in a tweet) arerecommended to connect/join in a social network. Same or similar phrasesare detected in any manner such as word/keyword comparison, and then amessage or any communication is sent to users that have provided thesame/similar phrase. Furthering the example, a popup is displayed on theuser's social network page that provides a list of users who have madethe same or a similar comment, and the user is asked if he wants toinvite the other users to join his network or to join their networks. Insome embodiments, a message/tweet is sent to both asking if they want to“connect.” In some embodiments, when misinformation is detected in aperson's comment, a message is sent to users in network saying thisperson said that and the fact check result shows it to be wrong.

In some embodiments, entity/validity ratings are based on relationshipswith other entities (including the web described above). Therelationships are able to be based on same cable network or samecompany. Using the web above, for example, if entities say the samemisinformation, they become linked together or connected and theirratings become related or merged.

In some embodiments, a user whose validity rating is below a lowerthreshold is automatically de-friended/disconnected/de-linked. In someembodiments, others are prompted with a question if they would like todisconnect from the user whose validity rating is below a lowerthreshold. In some embodiments, the user with a low validity rating isput in “time out” or his status remains a friend but a non-full friendstatus. For example, although the user with the low validity rating isconnected, he is not able to comment on a connected user's page. Inanother example, the capabilities of the user are limited on a socialnetworking site if his validity rating drops below threshold.

FIG. 10 illustrates a flowchart of a method of affecting a user based ona validity rating according to some embodiments. In the step 1000, avalidity rating of a user is determined to be below a threshold. In thestep 1002, the user is affected; for example, the user's access to webpages (e.g., social network) is restricted. In some embodiments,additional or fewer steps are implemented.

In another example, related to the web of lies, people are grouped(e.g., become contacts) if they send/say the same misinformation (maynot even know each other, but if they say “global warming is a hoax,”they join the same contacts as others who said same thing). In someembodiments, people who use the same phrase or quote (not necessarilymisinformation) become friends or are asked if they would like to becomefriends as someone who said the same thing. In some embodiments, userswith the same or similar validity rating are connected or asked if theywould like to connect.

FIG. 11 illustrates a flowchart of a method of connecting users based onsimilar content and/or validity rating according to some embodiments. Inthe step 1100, information is compared to determine a match. Forexample, user comments are compared to determine if they have said thesame thing such as “49ers rule!”. In some embodiments, onlymisinformation or other negative characteristic comments are compared.For example, a database stores comments that have been fact checked anddeemed inaccurate as well as the user that made the comment. Then, thosecomments are compared to determine if there are any matches betweenusers. In some embodiments, user validity ratings are compared as well.In some embodiments, users are grouped by validity rating (e.g.,validity rating is stored in a database and sortable by validityrating). In some embodiments, the validity ratings are exactly matched(e.g., all users with a validity rating of 7.0 are matched), and in someembodiments, ranges of validity ratings are matched (e.g., all userswith a 7.0 to 7.5 are matched). In some embodiments, opposite commentsare searched for. For example, a comment that says “raising taxes hurtsthe economy” and an opposite comment of “raising taxes helps theeconomy.” These comments are able to be considered an opposite match,which can then be used to join people with opposing views. In the step1102, users with matching comments and/or validity ratings are“connected” or asked if they would like to “connect” (e.g., join eachothers social network). In some embodiments, the steps occur inreal-time; for example, immediately after the user tweets, “49ersrule!,” connection suggestions are presented based on the implementationdescribed herein. Additional information is able to be provided to theusers such as the matching comment, the validity rating of the otheruser, and/or any other information. In some embodiments, additional orfewer steps are able to be implemented.

Additional Implementations

In some embodiments, mapping information is fact checked. For example, acamera device (e.g., augmented reality camera or vehicle camera) is usedto confirm traffic information on a map. Furthering the example, if amap indicates that traffic is going “fast” (e.g., over 50 mph), yet avehicle camera indicates the traffic is stopped, then an alertindicating the fact check result of “bad traffic information” is able tobe presented. In another example, if a map indicates the traffic acertain way, but a user's GPS (e.g., stand alone device or smart phoneGPS) indicates traffic differently, then an alert is provided to otherusers. In another example, accident information is fact checked bycomparing news information and/or police reports. In some embodiments,based on the fact check result, a corrected route is provided. Forexample, after fact checking a route, it is determined the traffic isnot bad for a particular road that was supposedly bad, so the route nowincludes that road. Fact checking of the mapping information is able tooccur periodically, when new information becomes available, or at anyother time. In some embodiments, mapping information from differentsources is compared. For example, G Maps indicates that traffic isflowing at 65 mph; however, A Maps shows that traffic is only going 35mph. The information from each source is compared (e.g., determine anydifferences), and analysis is performed to determine which is moreaccurate. For example, verification of either is searched for usingdirect knowledge (e.g., using vehicle camera or a camera positioned onthe side of the road or elsewhere to view traffic). Or a newsorganization is contacted for additional information. In someembodiments, the mapping information and fact checking results areshared among contacts in a social network. In some embodiments, themapping information is fact checked using social networking sourceinformation (e.g., information from contacts). In some embodiments,flying devices (e.g., drones) are utilized to provide information forfact checking. For example, the drones take images and/or videos oftraffic conditions and provide the images and/or videos as sourceinformation for comparison. In some embodiments, when an accident orother traffic issue occurs, a drone is able to be automatically directedto verify the issue by flying over to the area and acquiringinformation. For example, a user texts that an accident has occurred onInterstate X. The drone automatically receives/retrieves thisinformation, and flies into position to take pictures of the locationincluding traffic analysis. In another example, a device (e.g., a user'smobile device or a vehicle device) determines that user's vehicle ismoving much slower than the speed limit, so the device automaticallycommunicates with a drone (either directly or through a server), and thedrone utilizes GPS information of the vehicle to move into position toanalyze the traffic issues. The information acquired by the drone isthen dispersed to be used as source information. In some embodiments, aserver automatically determines the nearest drone to the position of theuser device, and directs only that drone to move to acquire information.

FIG. 12 illustrates a flowchart of a method of fact checking mappinginformation. In the step 1200, mapping information is analyzed (e.g.,monitored and processed). In the step 1202, the mapping information isfact checked. In the step 1204, a fact check result is presented. Insome embodiments, fewer or additional steps are implemented.

In some embodiments, an icon changes from a happy face to a sad face asmisinformation is given by an entity. In some embodiments, an image of aperson is changed from smiling to sad/angry. The fact checking systemcollects 2 to 5 different images of the person by detecting the person(e.g., facial recognition). Then, the system searches/crawls the web forpictures of the person using templates of smile, frown, angry face,tears, tense, stoic, neutral to do the searching. The appropriatepictures are retrieved and stored. The appropriate image is displayedwhen the misinformation calculation result is in range. For example,when zero misinformation is detected, a smiling face is displayed, andwhen 3-6 misinformation comments are detected the face displayed is afrowning face, and above 6 is a crying face. In some embodiments, tearsor other items are added to an image if the image cannot be found. Forexample, a sad image cannot be found, so tears are added to a neutralimage that was found.

FIG. 13 illustrates a flowchart of a method of using an icon to indicatea validity rating or the validity of information provided by an entityaccording to some embodiments. In the step 1300, one or more images ofan entity are acquired. In the step 1302, the entity's validity ratingis determine or the validity of the entity's comments is analyzed. Inthe step 1304, as the entity's validity rating changes or the validityof the entity's comments are analyzed, the image presented changes. Insome embodiments, additional or fewer steps are implemented.

In some embodiments, medallions/medals, tokens, ranks, points, and/orother awards/honors are provided based on user fact checking actions.For example, a user is awarded a different token for providing anaccurate fact check result for different items. Furthering the example,a user receives a “donkey” token for fact checking an item from a memberof the Democratic party, and an “elephant” token for fact checking anitem from a member of the Republican party. In some embodiments, theitem has to be an item not previously accurately fact checked (forexample, a comment by the President previously not fact checked). Insome embodiments, the fact check result is verified automatically,manually or a combination of both. In some embodiments, the userprovides the fact checked comment or identification information of thecomment as well as support for the fact check result (e.g., a websiteconfirming or disproving the comment). In some embodiments, the usermust perform a specified number of fact checks before receiving a token(e.g., 5 fact checks of Democrats to receive a “donkey” token).Additional tokens are able to include, but are not limited to: a“donk-phant” for fact checking both Democrats and Republicans, a “prez”token for fact checking the President, a “sen” token for fact checking amember of the Senate, a “house” token for fact checking a member of theHouse of Representatives, and a “news” token for fact checking anewscaster. In some embodiments, there are different levels of tokens.For example, one level of tokens is for actually fact checking, and asecond level is for merely flagging content as false, questionable, oranother characterization, and when the content is fact checked, a useris rewarded for being accurate. For example, if a user flags a commentas questionable, and then the comment is proven to be false, the user isawarded one point towards five points to obtain a second-level token. Insome embodiments, a user is penalized (e.g., points lost or demoted) forincorrectly flagging an item and/or providing an incorrect fact checkresult.

FIG. 14 illustrates a flowchart of a method of awarding honors for factchecking according to some embodiments. In the step 1400, a user factchecks or causes (e.g., flags) information to be fact checked. In thestep 1402, the user fact check or flag is analyzed/verified. In the step1404, the user is rewarded for a valid fact check. In some embodiments,fewer or additional steps are implemented.

In some embodiments, as a user acquires tokens, his label/title changes.For example, the user begins as a level 1 fact checker and is able toincrease to reach a level 10 fact checker if he acquires all of thepossible tokens. In some embodiments, users are able to specify the typeof label/title they receive. For example, users are able to specify“middle ages” which begins the user as a “peon” and goes up to “king.”Other examples include, but are not limited to: Star Wars (ewok to jediknight or storm trooper to sith lord (good/evil)), police (recruit tochief), military (cadet to captain), political (mayor to president). Byenabling the user to specify the set of labels or titles, additionalenjoyment occurs for the user. In some embodiments, a set of labels ortitles is generated for a group (e.g., social network group). Forexample, user X generates a football-labeled fact checking group whichstarts users as “punters” with the goal of becoming a “quarterback.”

In some embodiments, the label/title is based on the tokens, validityrating and/or other fact checking. A user's label/title is able to moveup or down based on the acquired tokens, validity rating and/or otherfact checking. For example, if a user acquires several tokens, but thenprovides misinformation several times, a token is able to be taken away.In some embodiments, users are provided additional features or benefitsfor a higher label/title. For example, a user with a level 8 factchecker label is provided contact information of several members of thenews media, whereas a level 1 fact checker is not provided thisinformation. Other benefits, awards and/or rewards are able to beprovided, such as monetary or item prizes. In some embodiments, thelabel/title is able to be used as a filtering tool for searches (e.g.,employee searches by employers). For example, an employer is able tosearch for candidates with “computer engineering skills” and “at leastlevel 5 fact checker.”

In some embodiments, users are rewarded for providing factually accurateinformation. For example, if a user tweets 100 times (and each of thetweets if fact checked by a fact checking system), the user receives areward such as a token or any other reward. In some embodiments, theinformation fact checked has to meet a specified criteria to qualify forcounting toward the reward. For example, the user is not able to tweet awell known fact 100 times and receive a reward. In some embodiments,steps to prevent cheating are implemented (e.g., monitoring forredundancy). In some embodiments, the information provided by the userhas to be directed to a specific topic (e.g., politics). In someembodiments, the information provided by the user needs to include akeyword to be fact checked to receive a reward. In some embodiments,only information with a specific label (e.g., hashtag) is fact checkedand count towards a reward.

In some embodiments, fact check swarms are able to be implemented. Usingsocial media (e.g., Twitter®), one or more users are able to encourageand/or trigger a fact check swarm such that many users attempt to factcheck information (e.g., a speech). Those that participate in the factcheck swarm are able to be recognized, awarded a prize, or providedanother benefit. For example, a user sends a tweet with a specifichashtag and/or other information regarding information to fact checkswarm. The users who receive the tweet are then able to participate inthe fact check swarm by researching elements of the information andproviding fact check results related to the information (e.g., bytweeting a snippet, a fact check result, and a cite to source(s) for theresult). The users in the swarm are then able to agree or disagree withthe result. If enough (e.g., above a threshold) users agree with theresult, the result is accepted and presented (e.g., tweeted or displayedon a television) to users outside of the social network.

FIG. 15 illustrates a flowchart of a method of touchscreen fact checkingaccording to some embodiments. In the step 1500, information ismonitored. In the step 1502, the information is processed. In the step1504, the information is fact checked, after detecting a touch of thetouchscreen (or a button or other implementation). In the step 1506, afact check result is provided. In some embodiments, additional or fewersteps are implemented.

In some embodiments, a touchscreen input is utilized for fact checking.When a user wants to flag content (e.g., a commentator talking) toindicate the information is questionable and/or to receive a fact checkresult, the user taps the touchscreen, and the last n seconds of contentare used for fact checking. For example, the content is continuouslymonitored and processed, and the fact checking system is able toretrieve previously processed information to perform the fact check.Furthering the example, a commentator is talking in a video, a user tapsthe screen, and the previous 10 seconds of content are fact checked. Insome embodiments, an additional time (e.g., 5 seconds) is fact checked.In some embodiments, the fact checking system determines the currentsegment. For example, the commentator says, “this project is a mess, itis $5B over budget.” The user taps the screen at “$5B” in the video. Thefact checking system had determined or determines that the currentsegment is “it is $5B over budget,” so that segment is fact checked. Insome embodiments, the current segment or a previous segment (e.g., toallow a delay of the user thinking) is fact checked. In someembodiments, the user is able to highlight closed caption content forfact checking. In some embodiments, when a user taps the touchscreen, alist of recent/current segments is displayed (e.g., pops up), and theuser is able to select one or more of the segments by tapping again. Insome embodiments, the list is displayed on a second or third screen. Insome embodiments, the list is based on time (e.g., most recent) and/orpriority (e.g., most relevant). In some embodiments, content ismonitored and processed, but the content is only fact checked when auser touches the touchscreen (or utilizes any other input mechanism). Insome embodiments, the user is able to use the touchscreen to select orhighlight text, information or a communication to have thattext/information/communication fact checked. For example, a user taps atweet on a screen to have the tweet fact checked. In another example, auser highlights text on a social networking page to have the text factchecked.

In some embodiments, content feeds are modified based on fact checking.Content feeds are fact checked, and a content feed with the highestfactual accuracy rating is presented on top/first. Factual accuracy andtime/date information are able to be combined for ranking/orderingcontent feeds.

In some embodiments, fact checking results are presented one after theother or in chronological order as a news/activity feed (and presentedvia social media/networking).

In some embodiments, fact checking information is displayed on a pagemostly (e.g., 95% or more) hidden behind the main content. The user canthen click on the page to view the fact check information.

In some embodiments, what time the misinformation was said is includedin timeline format or another format.

In some embodiments, misinformation is turned into jokes automaticallyto send to friends. In some embodiments, misinformation is turned into apostcard or greeting card. The misinformation is turned into a jokeand/or card by including the misinformation with a matching image and/ortemplate. The match is able to be made using a keyword or any othermanner. For example, if the misinformation is from Politician Z, acaricature of Politician Z is included as well as the misinformation andthe fact check result or a correction of the misinformation. In someembodiments, additional text, audio, images and/or video is providedsuch as an “oops!” sound or text, or silly music or any other effect toadd humor.

In some embodiments, the sources are rated using a rating system so thatsources that provide false or inaccurate information are rated as pooror unreliable and/or are not used, and sources that rarely providemisinformation are rated as reliable and are used and/or given moreweight than others. For example, if a source's rating falls or is belowa threshold, that source is not used in fact checking. In someembodiments, users are able to designate the threshold.

In some embodiments, comments are classified (e.g., high/false,mid/misleading, low/unsupported), and users are able to select whichclassification of information to exclude or receive. In someembodiments, “high” excludes only false information, “mid” excludesfalse and misleading information, and “low” excludes false, misleadingand unsupported information. In an example, user A accepts allinformation, but user B excludes only false information. Wheninformation is excluded, it is muted, crossed out, blacked out, notprovided, deleted, not transmitted and/or any other exclusion.

In some embodiments, fact check results are displayed when a user visitsa page (or views other content such as a video or a television show)based on previous fact checks done by/for other users. For example, UserA visits Webpage X, and a selectable/clickable link appears for the userto see the fact check result that was done by the fact check system forContact B of that page. In some embodiments, only fact checks performedby/for contacts of the user are displayed. In some embodiments, factchecks performed by/for anyone are displayed. In some embodiments, onlymanual fact checks are displayed, only automatic fact checks aredisplayed (e.g., automatically performed by the fact checking system) oronly automatic fact checks that have been manually reviewed aredisplayed. In some embodiments, the user is able to select to have afact check performed by the fact checking system using the user'ssources and compare the results with the previously performed factcheck(s). In some embodiments, only differences between the fact checkresults are displayed. In some embodiments, sources/criteria for theuser's fact check implementation is automatically compared with aprevious fact check's sources/criteria, and the user's fact check isonly performed if the user's fact check sources/criteria is different(e.g., substantially different) from the previous fact check'ssources/criteria. Substantially different is able to be determined basedon the number of different sources (e.g., number of different sourcesbelow a threshold), the quality of the differing sources (e.g., allsources have a 10 reliability rating), and/or any other analysis. Forexample, if the user's sources are the same except for one additionalapproved website, then the user's fact check and the previous fact checkare considered not to be substantially different.

In some embodiments, users receive benefits by fact checking content. Insome embodiments, users register to fact check and/or use their socialnetworking identification for fact checking and receiving benefits. Forexample, a user agrees to fact check a television program for freeaccess to the television program. In another example, a user fact checksa television program and is able to watch the next television programcommercial-free. In another example, a user agrees to fact check aprogram, and is provided the program is streamed to the user for free.Any benefit is able to be provided, including, but not limited to,commercial-free, shortened/fewer commercials, extended content, a period(e.g., month) of free cable/Internet access, program-specific access forfree (e.g., access to News Show X), discounted access (e.g., 50% off),free access to related or unrelated content and/or any other benefit.For example, if the user fact checks News Show X, then they are givenfree access to News Show Y. In another example, if the user fact checksNews Show X, they are given commercial free viewing of the next footballgame of their favorite team. In some embodiments, users are presentedselectable benefits from which to choose. For example, a user is offereda free movie, free sporting event programming or a 50% off download of anew release game, if they fact check News Show X. In some embodiments,the user is required to fact check a certain amount of content and/orreceive an accuracy rating above a threshold to receive the benefits.For example, a user agrees to fact check News Network X's content forfree access to the content. If the user abuses the agreement, and doesnot fact check the content or provides inaccurate fact check results,then the user's access is terminated. If the user provides accurate factcheck results, then the user is able to continue to receive free access.The user is able to fact check the content in any manner. For example,the user is able to manually fact check the content and provide theresults to a central or distributed fact checking system. In anotherexample, the user is able to utilize an automatic fact checkingimplementation that the user has modified (e.g., by selecting sources,monitoring rules, processing rules). In another example, users aregrouped or form groups to fact check content (e.g., crowdsourcing), sothat the groups work together to generate fact check results. Thebenefits are able to be applied to any type of content/services. Forexample, users of a social networking service are able to receiveexpanded access for fact checking, or no advertisement browsing as abenefit for fact checking, and/or any other benefits. In additionalexamples, users who agree to fact check YouTube content or provide aspecified number (e.g., 10) accurate fact check results, are allowed towatch YouTube videos without commercials for a day, or users who factcheck other users' Facebook® pages do not have any advertisementsdisplayed when they browse Facebook® or listen to a music playingservice such as Pandora.

FIG. 16 illustrates a flowchart of a method of automatically coding aweb page with fact check results according to some embodiments. In thestep 1600, information is analyzed. For example, a web page or a socialnetworking page is analyzed. Analyzing is able to include any analysisas described herein such as processing and/or parsing. In the step 1602,the information or the processed information (e.g., sentence segments)is fact checked as described herein (e.g., by comparing the informationwith source information). In the step 1604, the fact check results areautomatically coded within the information (e.g., web page).

Automatically coding the fact check results into the web page is able tobe performed in any manner. For example, an application on a serverdevice or other device is programmed to generate a file such as aWeb-loadable file, including, but not limited to: xhtml, mhtml, maf,asp, aspx, adp, bml, cfm, cgi, ihtml, jsp, lasso, pl, php, rna, r, rnx,ssi, xml, atom, eml, rss, metalink, markdown, shuttle, and/or json-ld.Furthering the example, the server device generates and names the filewith any naming convention (e.g., webpagenamefc1) and applies theappropriate extension (e.g., .asp), and then automaticallywrites/includes programming language in the file.

For example, an application automatically writing an html file wouldbegin with the <html> tag. The application also includes/writes the webpage content (e.g., text, web page tags for images, videos, and/oraudio, other web page tags, and/or any other formatting or substantiveinformation) in the file being automatically generated.

In some embodiments, a new web page file is generated by extractingcontent (including code/tags) from the original web page and adding factcheck results in the appropriate locations, and saving the web page withthe fact check results as a new file or replacing the original file withthe fact checked version. In some embodiments, after the new/modifiedpage is generated, a user is prompted to select between the two whenaccessing the web page. In some embodiments, the original web page ismodified to include redirect code or a link to the new page with thefact check results. In some embodiments, the original web page ismoved/replaced by a web page which presents the user with the option toselect either page. In some embodiments, a web page is fact checked anda new web page is coded with the results when a user attempts to accessthe web page (e.g., clicks on a link to visit a web page or types in theaddress in a browser), such that before the web page loads, the web pageis fact checked and coded. In some embodiments, the web page is factchecked after the page loads as it typically would load. In someembodiments, after the web page loads, it is refreshed automatically toshow the newly coded page with fact check results. In some embodiments,the web page is fact checked preemptively as described herein.

In an example of generating a new web page by extracting content, thecode of an original web page is:

<html> <body> <h1>Vaccines Provento Cause Autism</h1> <p>Based on astudy in Britain, vaccines have been proven to cause autism. More...</p><a href=″http://www.fakevaccinestudy.com″>This is a link to the study.</a> <img src=″vax.jpg″ alt=″fkvaxstdy.com″ width=″100″ height=″150″></body> </html>

The code above is copied, stored and fact checked, and a new web page isgenerated, including the fact check results:

  <html>   <body>   <h1>Vaccines Proven to Cause Autism</h1>   <h1style=″color:red″>FALSE</h1>   <p>Based on a study in Britain, vaccineshave been proven to cause autism. More...</p>   <pstyle=″color:red″>This study was discredited and retracted. For moreinformation, ...</p>   <a href=″http://www.fakevaccinestudy.com″>This isa link to the study.</a>   <img src=″vax.jpg″ alt=″fkvaxstdy.com″width=″100″ height=″150″>   </body>   </html>As shown in the example, the html code is modified to include the factcheck results which are coded in the appropriate locations, so that whenthe modified web page is displayed, the user will see that the headingis false and that the study was discredited and retracted. Any othermodifications to the web page are able to be implemented.

In some embodiments, dynamic code is coded into a web page whichretrieves the fact check results from another source (e.g., a database).In some embodiments, the fact check results are stored in a database (orother storage location) which is accessible using programming code(e.g., Cold Fusion or ASP). For example, a Cold Fusion call retrievesdata stored in a database. Similar to the code above, exemplary dynamiccode is:

<html> <body> <h1>VaccinesProven to Cause Autism</h1> <h1style=″color:red″>FALSE</h1> <p>Based on a study in Britain, vaccineshave been proven to cause autism. More...</p> <cfquery name=″fcQuery″datasource=″factcheckresults″>  SELECT cur status  FROM fcresults  WHERElocation =1 </cfquery> <cfoutput query=″fcQuery″>  #cur status#<br /></cfoutput> <a href=″http://www.fakevaccinestudy.com″>This is a link tothe study. </a> <img src=″vax.jpg″ alt=″fkvaxstdy.com″ width=″100″height=″150″> </body> </html>Stored in the database in the cur_status cell, is, “This study wasdiscredited and retracted. For more information, . . . ” The cfoutputcommand displays the fact check status in the web page after retrievingthe status from the database. As described herein, any data is able tobe stored in a database and retrieved to be displayed. For example, allof the text of an original web page is stored in a database in readingorder as well as fact check results, and by using more complex coding,the information stored in the database is able to be retrieved anddisplayed in the appropriate order/formatting, so that the coded webpage appears to the user similar to the original web page with theinclusion of the fact check results.

In some embodiments, only certain content of a web page is fact checked.For example, a specific tag is searched for (e.g., body and/body or hland/hl), and the content found within the tags is fact checked. Inanother example, a specific tag is searched for (e.g., img), and thecontent associated with the tag is fact checked. In some embodiments,certain content is excluded from being fact checked (e.g., by detectinga tag and not fact checking the content associated with or betweentags). In some embodiments, web pages that are linked (directly linked)to the current web page are also fact checked (e.g., by beingautomatically accessed and retrieved by the system) when the current webpage is fact checked (or queued to be fact checked at a later time), anda status, score, rating and/or other information is coded in the currentweb page (e.g., next to the link or in a bubble that appears during amouse over). For example, using the code from above:

...   <a href=″http://www.fakevaccinestudy.com″>This is a link to thestudy. </a> <p style=″color:red″>Fact check rating F </p> ...The code above shows a rating of an “F” after fact checking the studyweb page and determining the factual accuracy of the study web page isbelow the lowest threshold or any other manner of determining therating. In some embodiments, the color of the link is modified orre-coded based on the factual accuracy of the linked web page. Forexample, the color attribute of the link is modified to green when thefactual accuracy of the linked web page is equal to or above a firstthreshold, yellow when the factual accuracy is below the first thresholdbut equal to or above a second threshold, and red when the factualaccuracy is below the second threshold. In some embodiments, confidencescores are utilized in conjunction with factual accuracy. For example, alink attribute is changed to red only if the factual accuracy is below asecond threshold and the confidence score is above a confidencethreshold. Furthering the example, if a factual accuracy is equal to orabove a first threshold, and the confidence score is equal to or above afirst and/or second confidence threshold, then the link color attributeis modified to green, but if the confidence score is below the secondconfidence threshold, then the link color attribute is not modified. Ifthe factual accuracy is below the first threshold but equal to or abovethe second threshold, and the confidence score is equal to or above afirst and/or second confidence threshold, then the color attribute ismodified to yellow, but if the confidence score is below the secondconfidence threshold, then the color attribute is not modified. If thefactual accuracy is below the second threshold, and the confidence scoreis equal to or above a first and/or second confidence threshold, thenthe color attribute is modified to red, but if the confidence score isbelow the second confidence threshold, then the color attribute is notmodified. Any number of thresholds can be used, and anystyles/formatting/effects (e.g., color, highlighting, font,strikethrough, italics, underling, font size, blinking, shadows, 3Deffects) are able to be used based on any of the thresholds. In someembodiments, the link color is only changed if the factual accuracy isbelow a threshold or if the factual accuracy is below a threshold and aconfidence score is above a threshold. In some embodiments, a colorattribute is modified depending on the factual accuracy, and anadditional modification of the link is implemented depending on theconfidence score. For example, if a linked content has a factualaccuracy of 90% (e.g., equal to or above a first threshold) and aconfidence score of 90% (e.g., equal to or above a confidencethreshold), then the link color attribute is modified to green, but ifthe linked content has a factual accuracy of 90% (e.g., equal to orabove the first threshold) and a confidence score of 40% (e.g., lessthan the confidence threshold), then the link color attribute ismodified to green and the link font style attribute is modified toitalics or an asterisk is added near the link to indicate that althoughthe factual accuracy is above a threshold, the confidence in the factcheck is below the confidence threshold.

In some embodiments, a frame is generated to be placed on the currentweb page. The frame is able to be generated by modifying the web page toinclude a frame (e.g., add a <frame> tag). In some embodiments, coded inthe frame are the fact check results.

In some embodiments, the fact check results are coded in the web page inanother location instead of or in addition to near the fact checkedinformation. For example, the fact check results are coded at the top ofthe web page, at the bottom of the web page, as a link anywhere on thepage which when clicked takes the user to another web page containingthe fact check results, and/or any other location.

In some embodiments, the generated web page with fact check results isonly temporarily saved. For example, once a user closes the web page,the generated web page is deleted. In another example, the generated webpage is saved in a temporary folder, and as long as a cookie or otherrecord is on the user's computer, the generated web page will remainsaved but is deleted when the cookie or other record is deleted orexpires. In some embodiments, when the generated web page is deleted,the original web page is restored (e.g., by replacing the file orchanging a link to point to the restored file).

In some embodiments, the generated web page with fact check results isutilized for other users. For example, a first user visits a web page,and the web page is fact checked and coded/re-coded with the fact checkresults, and then a second user visits the web page, and in someembodiments, the re-coded web page is displayed for the second user. Insome embodiments, the current user is determined by being logged in(e.g., logged in to a social networking system which the fact checkingsystem has access to). In an example, only users connected to the uservia a social networking site are presented with the re-coded web page.For example, the social networking system or another system determinesif the current user accessing a web page is connected to the user forwhich the page was previously fact checked (e.g., by storing and/orsearching for user/connection information). If the users are connected,then the current user is presented with the re-coded web page. In someembodiments, the current user is presented an option of receiving there-coded web page, the original web page, and/or if the current userwould like the web page to be fact checked using his sources if hissources are different than the user for which the page was previouslyfact checked. In some embodiments, the previously determined fact checkresults (e.g., for a connected user) and the fact check results based onthe current user's sources are compared, and the differences areautomatically coded in a new web page including distinguishing features(e.g., different color or font size/style).

In an exemplary implementation, a web page (e.g., social networkingpage) is scanned by the application. Content, including web page tagsand other content, is captured by the application. The content is parsed(e.g., into phrases). The phrases are identified (e.g., each phrase isstored in a database in order based on location on the page and/or anidentifier such as a number for each phrase). The parsed content is factchecked, and the fact check results are associated with the identifier(e.g., given a matching identifier) or placed in the database in theappropriate location (e.g., associated with the corresponding factchecked phrase). Then, the web-page is re-written/coded including theweb page tags and other content as well as the fact check results usingthe identifiers or by writing the content in order using the database.

In another example, as the web page is analyzed and parsed, a modifiedweb page is generated (e.g., by copying the information from theoriginal web page to the modified web page each parsed phrase at a time,and the parsed information is also fact checked, and a fact check resultis input to the modified web page after each parsed phrase where thereis a fact check result). For example, an original web page is scanned(e.g., top to bottom/left to right), and any code or tags (starting atthe top-left and working down-right) are copied to a new web pageincluding the first phrase of text, then the first phrase is factchecked or it is determined if it is to be fact checked (e.g., based onformatting or any other determination), and if fact checked, then thefact check result (if one is obtained) is coded in the new web pageimmediately after the first phrase, then the second phrase is copied tothe new web page and is either fact checked or determined to be factchecked, and the result, if any, is coded in the new web pageimmediately after the second phrase, and so on (including copying text,tags, and/or other content and generating and coding fact check results)until the end of the original web page is reached. In some embodiments,the web page is analyzed/modified character by character, word by word,phrase by phrase, sentence by sentence, line by line, section by sectionor any other manner. In some embodiments, the step of copying contentfrom the original web page to the modified or new web page and the stepof fact checking information occur in parallel. In some embodiments, thefact check result is input into the modified web page before, after,above, below, over, and/or anywhere else in relation to the associatedfact checked content on the web page.

In another example, a source list or source criteria used for theprevious fact check is stored or identifying information of the sourceis stored (e.g., as metadata within the web page) and compared with thecurrent user's source list/criteria, and if they are the same, then thepreviously coded/generated web page is displayed. If they are not thesame, then a new fact check is performed, and a new web page isgenerated including only the new fact check results or the old and newfact check results. If the fact check results are different, then thenew web page (and/or source list/criteria in metadata) is also storedfor comparison with other future users. If the fact check results arethe same, a record of that is stored (e.g., in metadata) to specify thata different source list/criteria resulted in the same fact checkresults, and the new web page is deleted or is not saved/generated.

In some embodiments, fact check results are stored in metadata (e.g.,fact check results using a base set of sources for fact checking). Then,when a user visits the web page, a second fact check is performed usingthe sources specific to the user or other sources, and the results ofthe second fact check are compared with the results stored in themetadata. If the result of the second fact check is different, then thatresult is added to the metadata, and if the result is not different, theresult is not added. In some embodiments, additional information isincluded in the metadata such as a source description, time/dateinformation of when the fact check took place, user informationcorresponding to the fact check, and/or any other relevant information.

In some embodiments, a generic fact check result is coded in a web pageor metadata, but a tailored fact check result is generated for aspecific user, and the tailored fact check result is coded in a web pagestored locally on the user's device. In some embodiments, the tailoredfact check result web page is only stored locally on the user's device.

In some embodiments, the generated web page with fact check results issaved for all other users. For example, on the host site's server, theoriginal web page is deleted and replaced with the generated web page,so that anyone who visits the web page sees the fact check results withor without an updated fact check.

In some embodiments, the web page is re-coded when a user visits the webpage. In some embodiments, the user views the un-fact checked web page,and then a pop up is displayed to enable the user to view a modifiedfact checked web page (e.g., the pop up has a link which causes the pageto be refreshed to show the modified fact checked web page). In someembodiments, future users either see the original web page, the coded,fact checked web page, and/or are able to select between the two.

In some embodiments, the fact check results are automatically coded incontent such as microblogs (e.g., Tweets) and/or other communications.

In some embodiments, the method of coding the fact check results in aweb page utilizes other aspects described herein. For example, after themodified web page with the fact check results is displayed for the user,the user's reaction is acquired (e.g., by a web cam or camera phonecamera), and if the reaction is determined to be negative, the web pageis fact checked again, and a new modified web page is generated. In someembodiments, when the new modified web page is generated, the previousmodified web page is deleted. In some embodiments, when the new modifiedweb page is generated, the previous modified web page is stored to beused for another user (e.g., a user with the same sources). In someembodiments, when the new modified web page is generated, it alsoincludes the previous fact check results, so that the user is able tocompare the results.

In some embodiments, each time a user visits a web page (e.g., the sameweb page), the web page is fact checked again. In some embodiments,information is stored (e.g., in a cookie, log, cache, or other storage),so that the same web page is not fact checked again for the same user orfor other users with the same fact check criteria. In some embodiments,a determination is made to determine if the web page contains the samecontent as the previously fact checked web page. For example, althoughWeb Page X was previously fact checked for User A, Web Page X has beenupdated with new information, so Web Page X is fact checked again (oronly the updated information is fact checked), and a new web page iscoded. In some embodiments, previous versions of web pages are stored,which are used to compare with the current web page to determine anydifferences. In some embodiments, each time a different user visits aweb page, the web page is fact checked using that user's sources.

In some embodiments, prediction is utilized to fact check web pagesahead of time to expedite the processes of fact checking and the webpages loading. For example, pages linked to the current page and pageslinked to those linked pages are fact checked using generic sources, auser's sources, a user's contacts' sources and/or other sources. In someembodiments, the levels of linked pages to be fact checked is any number(e.g., user or system configured). For example, 4 levels of linked pagesare fact checked such that a web page four links away from the currentpage is fact checked when the user visits the current web page. In someembodiments, prediction of what pages to fact check is based on user orcommunity analysis. For example, based on the user's browser history, itis known the user typically visits Web Site X (which has several webpages of articles) at least once a day, so all or some of the articleson Web Site X are fact checked periodically (e.g., at 9 am each day orevery hour). In some embodiments, further analysis is performed, forexample, it is known that the user accesses Web Site X at 9 am everymorning on the weekday, so the fact check of the web pages on Web Site Xoccurs at least 1 second before 9 am (assuming fact checking the webpages of the site takes 1 second or less). In another example, theuser's preferences are taken into consideration for prediction. Forexample, the user is a big sports fan, so all articles (of Web Site X)classified as sports are fact checked ahead of time (e.g., systemmonitors for new sports articles and fact checks them immediately). Inanother example, when a user moves his mouse on or toward a link, thelinked page is fact checked before detecting a click/selection of thelink. Furthering the example, the system determines the locations of thelinks on the current page (e.g., by locating link tags and theirrespective pixel positions on the screen) and tracks the user's mouse orother input movements (e.g., by detecting previous and current pixelsthe cursor was/is on and connecting them to generate a path), includingdetermining if the cursor is over a link or if the cursor is movingtoward a link (e.g., by projecting a line along the current path of thecursor), and if a link is over or going towards a link, the link ispredicted to be clicked, so it is fact checked preemptively by thesystem. In some embodiments, when the user attempts to visit the webpage, the user is redirected to the modified web page with the factcheck results. In some embodiments, a user's eyes are tracked, and it isdetermined where the user's eyes are pointing, and any links near wherethe eyes are pointing are fact checked preemptively. For example, auser's eyes are detected to be pointing at position (x,y), so a box,circle or other shape is drawn/determined with (x,y) as the center point(e.g., (x−5,y), (x+5, y), (x, y−5, (x, y+5)), and any links with aposition within the shape are preemptively fact checked and coded.

In some embodiments, the fact checking system predicts which web pagesto fact check preemptively based on web page statistics (e.g., mostvisited web sites/pages based on hits/clicks, trending information, mostliked/disliked web sites/pages, most shared content such as video,images, social networking content, web pages. For example, most visitedweb sites are predicted to be visited in the future by the most users,so those pages are preemptively fact checked before less visited websites. In some embodiments, the prediction is based on web site/pagetype (e.g., news web pages which provide news are fact checked beforecompany web sites which merely provide company information.) Forexample, web sites or pages are classified manually or automaticallybased on the type of content or goal of the site. Exemplaryclassifications include: media site, news site, company site, socialnetworking site, personal blog, and/or any other classification. In someembodiments, further levels of classification are implemented, such asnews->sports->golf; news->finance; entertainment->movies->comedy. Insome embodiments, users are able to specify which sites/pages to predictto fact check. In some embodiments, one or more queues are implemented,and web pages are given a priority depending on theirtype/classification/popularity and/or othercharacteristic/quality/attribute.

In some embodiments, users are able to tag web sites/pages (or provideother input) for predictive fact checking/coding, and the web pages withthe most tags/selections (or above a threshold) are fact checked, andthe results are coded preemptively or in order of the mosttags/selections to the least. For example, a web browser includes a factcheck tag button which copies the web page address or other identifyinginformation and sends the information to a fact checking system fortabulation.

In some embodiments, web pages are fact checked and/or predictively factchecked using multiple different implementations (e.g., differentsources), and the different fact check results are coded in web pages.For example, a web page is fact checked using only conservative sourcesas a first implementation, only liberal sources as a secondimplementation, a mix of conservative and liberal sources as a thirdimplementation, and only sources with a validity rating above athreshold as a fourth implementation. Furthering the example, fourdifferent web pages (based on the same original page being fact checked)are coded with the different fact check results. In some embodiments,the results are compared before coding the pages with the fact checkresults, and if the results are the same among any of theimplementations, then that is stored (e.g., in metadata), so that fewerpages are generated and stored. For example, if the third and fourthimplementations generate the same fact check results, then only one webpage is coded with the fact check results, and metadata is storedindicating that those fact check results were generated using the thirdand fourth implementations. In some embodiments, when a user visits theweb page, he is able to select which fact check results are shown. Insome embodiments, which web page with fact check results (e.g., whichcoded web page) is shown is automatically determined based on usercharacteristics (e.g., based on social networking information, the useris a Conservative, so the first implementation fact check results areshown). In some embodiments, all of the different results are coded inthe same web page but with distinguishing characteristics (e.g., color).

In some embodiments, predictive fact checking/coding is based on anentity's (e.g., user, company, web site, media company, author) validityrating. For example, entities with low validity ratings (e.g., below athreshold) are fact checked first so that any further misinformation isindicated. In another example, entities with high validity ratings(e.g., above a threshold) are fact checked first since they have earnedearlier fact checking by providing factually accurate information. Insome embodiments, the validity rating is used in conjunction with otherinformation such as popularity or traffic to determine which sites topredict to fact check and code and/or in which order. For example,although Blogger X may have a very low validity rating, his visitortraffic is also very low, so not many people read his misinformation. Avery basic exemplary calculation of predicting which site to fact checkis (10−validity rating)+traffic score, where the validity rating is 1 to10 with 10 being the most factually accurate and the traffic score is 1to 100 with 100 being site with the most traffic, so that a heavytraffic site with factually inaccurate information is predicted to befact checked and coded with the results first. For example, Bob's Bloghas a 1 validity rating but a traffic score of 1, so it receives a totalscore of (10−1)+1=10. However, News Site X has a 5 validity rating but atraffic score of 99, so it receives a total score of (10−5)+99=104.Thus, in this example, the News Site X web page would be fact checkedand coded before Bob's Blog. In some embodiments, the validity ratingand other quantity (e.g., traffic score) are weighted equally ordifferently (either having a higher weight depending on theimplementation). In other examples validity rating, traffic, keywordinformation, time/date information, sharing information, classification,other information and/or any combination thereof are utilized todetermine which web pages to preemptively fact check and code. Forexample, the validity rating and a sharing score are utilized togenerate a total score, and the higher a web page's score, the morelikely or higher priority the page will be fact checked and codedpreemptively. In another example, the traffic score and classificationare utilized together to predict which pages should be fact checked andcoded first. In a more specific example, a web site has a low validityrating, a high traffic score, web pages with a recent creation time/dateand a high sharing score, so the web pages are a high priority for factchecking and coding compared to higher validity rating, lower traffic,older created, and/or lower sharing scored pages. In another example, aprediction score calculation includes: (50−validity rating)+trafficscore+time/date score+sharing score+classification score, where thevalidity rating is 1-50, the traffic score is 1-100, the time/date scoreis 1-50, the sharing score is 1-50 and the classification score is 0,10, 20, or 30, depending on the classification. Furthering the example,if a web page's author's previous content is very accurate, then thevalidity rating is 50, if a web page has heavy traffic, then the trafficscore is 100, if a web page has been recently created, then thetime/date score is 50, if a web page has been shared significantly viasocial networking, then the sharing score is 50, and exemplaryclassifications are: personal blog (0 points), company web page (10points), commentary blog/web page (20 points), and news web page (30points). The scores decrease if a web page's information falls belowspecified thresholds or using any other calculations to determine a webpage's scores. After web pages are given scores, the page with thehighest score is fact checked and coded first, then the next highest,and so on. In some embodiments, pages' scores are not compared againsteach other; rather, the scores are compared with other benchmarks (e.g.,if a web page's score is above a top threshold, it is classified to befact checked and coded with the highest priority). In some embodiments,the priority classifications based on total scores cause the web pagesto be placed in different queues for fact checking (e.g., highestpriority classifications are fact checked and coded by the fastestmachine, and lower priority classifications are fact checked and codedby slower machines, or highest priority classifications are fact checkedand coded before any lower priority classified web pages). In someembodiments, web pages classified in the top priority classification arefact checked using multiple implementations (e.g., first set of sources,second set of sources, third set of sources for fact checking topossibly generate varying results), and the lower priorityclassification web pages are only fact checked using one fact checkimplementation.

In some embodiments, predictive or preemptive fact checking and codingweb pages utilizes age or time/date of web pages. For example, newestweb pages are fact checked and coded first. In another example, oldestweb pages are fact checked and coded first. In another example, newsarticle web pages are analyzed based on a creation date/time (e.g.,stored in file metadata), and the web page with the newest creationdate/time is fact checked first, and the fact checking system continuesto older web pages. In some embodiments, separate fact checking systemsare implemented (e.g., one fact checks and codes newest web pages andanother fact checks and codes oldest web pages, and the implementationsattempt to meet in the middle).

In some embodiments, current events or other timing information isutilized to determine which content to fact check and code preemptively.For example, if it is determined that a Presidential election isupcoming (e.g., by comparing the current date with a database ofevents), then news articles with keywords (e.g., election, candidates'names) are fact checked and coded.

In some embodiments, the prediction is based on sharing information(e.g., after an article/web page/content is shared by a number of usersabove a threshold, then that content is fact checked and coded.

In some embodiments, the prediction is based on a topic. For example,all web pages related to the topic of politics are fact checked andcoded preemptively. In some embodiments, keywords are searched for, andif a web page contains the keyword or a sufficient number of keywordsabove a threshold, then the web page is fact checked and the results arecoded.

In some embodiments, the prediction is based on search information(e.g., the most searched for information). In some embodiments, searchresults of a search engine are fact checked and preemptively coded. Forexample, a user types in a search string. In some embodiments, before orafter the user submits the search, the web pages that would appear forthe search are preemptively fact checked and coded, so that when the webpage search results are presented, and the user selects a web page, theweb page has already been fact checked and the results are coded. Insome embodiments, the search results are displayed in a format thatenables the user to select from fact checked pages, non-fact checkedpages and/or fact checked pages with varying implementations (e.g., factchecked using Conservative sources, fact checked using Liberal sources).

In some embodiments, the user selects which web sites/pages to predictto fact check. For example, the user uses a GUI to select which websites/pages should be predictively fact checked. In some embodiments,the approval/disapproval schemes described herein forapproving/disapproving sources are able to apply to selecting web pagesto predictively fact check and code. For example, web pages visited by auser and web pages visited by contacts of the user are predictively factchecked and the fact check results are coded. Furthering the example, acontact visits a web page which is fact checked for the contact usinghis sources and is also fact checked and coded preemptively using theuser's sources.

In some embodiments, information that a web page has been fact checkedis coded in the web page. For example, if a web page is fact checked andthere are no factual inaccuracies, there may be no fact check results(although in some embodiments, factual accurate information is coded asfactually accurate), so a heading or other text/icon/image/content isable to be coded to indicate the web page has been fact checked.Furthering the example, in the upper right corner, a green check mark isable to indicate the page has been fact checked. In some embodiments,fact check statistics are coded in the web page. For example, anindication of the number of factually inaccurate phrases, factuallyaccurate phrases and questionable/unknown phases are coded in the webpage.

In some embodiments, a payment scheme is utilized to enable websites/users to pay for their web pages to be higher on a list to be factchecked (e.g., fact checked before other sites). For example, if a website wants their content fact checked so that when anyone visits theirweb site, the visitors will see fact checked content, the web site isable to pay a fee. In some embodiments, web sites/pages are classifiedin different priority classifications, including paid and unpaidclassifications, where the web pages in the paid classifications arefact checked and coded before the pages in the unpaid classifications.In some embodiments, the fee amount is tiered, and the highest tier isfact checked first. In some embodiments, a web site is able to purchasededicated fact checking such that a fact check implementation only factchecks content on that web site.

In some embodiments, the different predictive fact checkingimplementations are utilized together (e.g., using user preferences/webhistory, general web site popularity and cursor analysis).

FIG. 17 illustrates a flowchart of microblogging with fact checkingaccording to some embodiments. In the step 1700, a server or otherdevice monitors users' microblogs (e.g., Tweets). In the step 1702, eachmicroblog is processed (e.g., converted, parsed, or other processing).In the step 1704, each microblog or portion of a microblog is classified(e.g., as fact or opinion). The classification of the microblog is ableto be performed in any manner such as grammar analysis (e.g., detectingwords or phrases indicating whether the content is fact or opinion suchas “in my opinion”). In the step 1706, fact checking as described hereinis performed (e.g., monitoring/analyzing, processing and/or factchecking). In some embodiments, only fact-based microblog content isfact checked, and in some embodiments, all microblog content is factchecked. In the step 1708, the fact check results are utilized tosuggest which microbloggers users should follow. For example, the factcheck results are utilized to perform calculations to generate a factualaccuracy score for users, and then the factual accuracy score is able tobe used independently or in conjunction with other factors of whom tosuggest to be followed. In an exemplary calculation, if a microblog isdetermined to be factually inaccurate, a point is subtracted from themicroblogger's factual accuracy score, and if a microblog is determinedto be factually accurate, a point is added to the microblogger's factualaccuracy score. In some embodiments, only subtractions are performed toprevent manipulated inflation of a user's score. In some embodiments,only certain factually accurate microblogs receive a point (e.g., thefirst time a phrase or other content is fact checked). For example, aphrase is compared with a data structure which stores each phrase (oridentification information) as it is analyzed, and if a phrase (oridentification information) is already in the data structure, then thephrase has already been fact checked, so the phrase is not used to awardany more points. In some embodiments, each factually inaccurate item(e.g., phrase) in a microblog counts as a negative point for the user.Similarly, each factually accurate item counts as a positive point forthe user. For example, in some embodiments, a single microblog can onlyresult in −1 point even if it has multiple inaccurate comments, but insome embodiments, if a single microblog has 3 inaccurate comments, thenthe result is −3 points. In another example, the confidence score of theaccuracy or inaccuracy is factored in. For example, if a comment isconsidered to be factually inaccurate, but the confidence score is only50%, then (−1*0.50=−0.5 points), whereas a comment that is factuallyinaccurate with a confidence score of 97% is −1*0.97=−0.97 points.Similarly, the confidence score can be used for a factually accuratecomment (1*0.80=0.8 points). In some embodiments, a total score isdetermined for each microblog. For example, if a Tweet has onesentence/phrase that is factually accurate with a confidence score of70% and a second sentence/phrase that is factually inaccurate with aconfidence score of 90%, then the total score for that Tweet is(1*0.70)+(−1*0.90)=−0.2. In some embodiments, the microblog scores aretotaled for each user. For example, using a basic approach, if a usersends 100 Tweets, and 70 of them are factually inaccurate, then the userhas a score of −70. In some embodiments, users are ranked and/or aresortable. In some embodiments, the scores reset after a period of time(e.g., once a week, month, year). In some embodiments, the microblogscores are used in conjunction with other user characteristics/qualitiessuch as preferences, connections, and/or other personal information tosuggest whom to follow. For example, if a user is interested in sports,and there are two microbloggers who microblog on sports, but the firstmicroblogger has a factual accuracy score of negative two (−2) while thesecond microblogger has a factual accuracy score of negativeseventy-nine (−79), the user would be suggested to follow the firstmicroblogger before the second microblogger. In another example, amessage is sent to a user from a social networking site asking the userif he wants to follow/connect with another user, where the other userhas a factual accuracy score above a threshold and/or other matchingcharacteristics (e.g., same political affiliation). In some embodiments,users' icons, backgrounds, and/or post features/characteristics aremodified based on their factual accuracy score (e.g., red if factualaccuracy score is below a first threshold, yellow if the factualaccuracy score is equal to or above the first threshold but below asecond threshold, and green if the factual accuracy score is equal to orabove the second threshold. In some embodiments, the scores of themicrobloggers are displayed. Furthering the example, a list of users(including their factual accuracy score) to follow is provided to theuser with the most accurate on top and the least accurate on bottom. Insome embodiments, fewer or additional steps are implemented. In someembodiments, the order of the steps is modified.

In some embodiments, trending information (e.g., information such asarticles, web pages, blogs that have been searched for, clicked onand/or shared recently by a large/increasing number of users) isanalyzed, and only certain information (e.g., top 10 trending articles,as determined by analyzing web search information, social networkinginformation, and/or any other manner) is fact checked. After factchecking, the trending information is reorganized based on the factcheck results. For example, in some embodiments, the top 10 (based ontrending statistics) news articles are collected, analyzed and/or factchecked. In some embodiments, if an article is determined to beinaccurate (e.g., number of factually inaccurate segments/phrases abovea threshold or percentage of factually inaccurate segments/phrases abovea threshold), then its position in the list is modified (e.g., droppedone spot, removed from the list). In some embodiments, the modifiedposition depends on the factual accuracy result/score. For example, if afactual accuracy of an article is equal to or below a first thresholdand above a second threshold, then the article drops one spot in thelist, and if the factual accuracy is below or equal to the secondthreshold and below a third threshold, the article drops multiple spotsin the list, and if the factual accuracy is below or equal to the thirdthreshold, then the article is removed from the list. When an article isremoved from a list, another article is added to the list (e.g., articlein the 11^(th) trending spot), and the added article is fact checked,and the list may be modified again. In some embodiments, the items inthe list are analyzed concurrently. In some embodiments, the items inthe list are analyzed sequentially starting from the top of the list orthe bottom of the list. In some embodiments, all or portions of the listare analyzed before the list is modified. In some embodiments,confidence scores are taken into account as well. For example, if anarticle is determined to be factually inaccurate, but the confidencescore is low, instead of removing the article completely, the article isdropped one or more spots. Furthering the example, if a factual accuracyof an article is below or equal to a first threshold and above a secondthreshold and the confidence score is below a confidence threshold, thenthe article is not moved, but if the factual accuracy of an article isbelow or equal to the first threshold and above the second threshold andthe confidence score is greater than or equal to the confidencethreshold, the article drops one spot in the list, and if the factualaccuracy is below or equal to the second threshold and above a thirdthreshold and the confidence score is below a confidence threshold, thearticle drops a first multiple spots (e.g., 2) in the list, but if thefactual accuracy is below or equal to the second threshold and above thethird threshold and the confidence score is greater than or equal to theconfidence threshold, the article drops a second multiple spots (e.g.,5) in the list, and if the factual accuracy is below or equal to thethird threshold and the confidence score is below a confidencethreshold, then the article drops multiple spots in the list, but if thefactual accuracy is below or equal to the third threshold and theconfidence score is greater than or equal to a confidence threshold,then the article is removed from the list. In some embodiments, if theconfidence score is not above a threshold, no effect (e.g., drop) istaken. In some embodiments, there are multiple confidence thresholds(e.g., a first, a second and a third confidence threshold), anddepending on what the confidence of a fact check result is compared tothe thresholds affects the outcome of the article (e.g., above firstconfidence threshold has a greater effect than below the thirdconfidence threshold).

FIG. 18 illustrates a flowchart of a method of fact checking utilizingfact checking analytics according to some embodiments. In the step 1800,fact checking as described herein is performed (e.g.,monitoring/analyzing, processing and fact checking). In the step 1802, afact check result is presented. For example, an initial fact checkresult is a generic, non-user-specific result, but subsequent fact checkresults may be tailored or modified otherwise. Furthering the example, ageneric result of true, false, or any other characterization isinitially presented.

In the step 1804, a reaction of a user to the first fact check result isacquired. The reaction of the user is able to be acquired in any mannersuch as by asking the user if they agree with the result, and the userinputting (e.g., clicking, saying, touching) “yes” or “no.” In anotherexample of acquiring the user's reaction, a camera/microphone device(e.g., camera/microphone in a smart phone/tablet/tv or webcam) monitorsthe user reaction and utilizes software capable of recognizingexpressions on a user's face/body (e.g., smiling, frowning, nodding,head shake, shrug, laughing, crying) or a user's sounds, tones, words(e.g., words of agreement/approval or disagreement/disapproval). Inanother example, users are able to like/dislike, give thumbs up/down, orprovide other reactions to fact check results. In another example, auser's blood pressure, heart rate, perspiration, oxygen levels, and/orother bodily information is measured (e.g., using a heart rate monitorcontained within a smart watch or other device). Based on studies, anelevated heart rate may indicate the person strongly disagrees with theresult from being frustrated. Furthering the example, the smart watchworn by the user and the device (e.g., smart phone, television)presenting the information (e.g., news report) communicate (e.g., aresynchronized) with each other, so that if the user's heart rate goes upsignificantly, the device presenting the information receives thisinformation and recognizes that it is in reaction to the current factcheck results. The synchronization is able to be based on the timing ofitems such as keeping track of when fact check results are displayed andthe user's heart rate, and if the heart rate rises within 10 seconds ofthe fact check result, then it is determined the heart rate change isbased on the fact check result. In some embodiments, analysis isperformed to determine how long a specific user's heart rate takes torise and how much (e.g., by providing training results and monitoringthe user's heart rate). In some embodiments, the heart rate analysis isa preliminary determination of the reaction, and a follow-up is used tomake a full determination. For example, if the user's heart rate risesafter a fact check result is displayed, the user is then prompted toanswer if they agree or disagree with the fact check result. In someembodiments, the prompt to the user is not displayed unless the heartrate alert (or other preliminary item) is triggered. In someembodiments, training/learning of the body analysis (e.g., heart rate)is performed. For example, the system monitors a user's heart rate for aweek along with the information (news stories) and fact check resultsbeing presented to the user to determine if there are patterns beforetaking steps based on the reactions.

In the step 1806, if the reaction is positive (e.g., smile, agree,thumbs up, no heart rate change), then no change to the fact checkingprocess occurs. In the step 1808, if the reaction is negative (e.g.,frown, disagree, thumbs down, heart rate rises), the fact checkingimplementation is modified.

In some embodiments, modifying the fact checking implementation includesmodifying the sources used for fact checking. For example, a first setof sources are used for fact checking initially, and if the reaction isnegative, a second set of sources are used for fact checking. In someembodiments, the second set of sources are narrower (e.g., fewer), andin some embodiments, the second set of sources are broader (e.g., more)than the first set of sources. In some embodiments, the second set ofsources are limited to sources with a factual accuracy/reliabilityrating above a threshold. In some embodiments, the second set of sourcesare selected by the user and/or another entity (e.g., web site owner,social networking system). In some embodiments, the user is able toselect sources to use in addition to the first set of sources for factchecking. In some embodiments, the fact check is repeated using themodified set of sources, and a result is displayed such as “confirmed”or the new result is presented. In some embodiments, if the result isconfirmed, nothing new is displayed.

In some embodiments, modifying the fact checking implementation includesutilizing sources based on social networking. For example, the first setof sources used for fact checking are sources approved by the user, andthe second set of sources used for fact checking are sources approved bythe user and sources approved by the user's contacts. In anotherexample, the approval/disapproval implementations described herein areutilized to determine different sources to utilize for fact checking,and the different approval/disapproval implementations are able to beutilized sequentially as negative reactions are detected (e.g., after afirst negative reaction is detected, approved sources approved whilelogged in are used, and after a second negative reaction is detected,approved sources specifically selected as approved are used, and after athird negative reaction is detected, approved sources of the user andcontacts of the user including resolving any conflicts betweenapprovals/disapprovals are used). In some embodiments, each differentset of sources is limited to the specified type of sources (e.g., onlysources approved while the user was logged into the social networkingsite). In another example, a database links sources of social networkingcontacts to be used for fact checking.

In some embodiments, modifying the fact checking implementation includespresenting a tailored fact check result based on user information (e.g.,job, income, political affiliation, activities, preferences accessiblein a data structure or analysis of online data). For example, instead ofmerely presenting “true” when fact checking “the ocean's temperature isrising,” since one of the user's activities is scuba diving asdetermined by analyzing the user's social networking information, theresult is tailored to say, “true, and this is having a significantimpact on coral reefs around the world.”

In some embodiments, the process of modifying the fact checkingimplementation based on the user's reactions repeats. For example, ifthe user reacts negatively when the next fact check results arepresented, then another set of sources are used for fact checking,and/or a tailored or modified-tailored fact check result is presented.In some embodiments, the process of modifying the fact checkingimplementation stops after a threshold is reached. For example, afterthree modifications of the fact checking implementation, no furthermodifications are made.

In some embodiments, if a user continues to react negatively to the factcheck results (e.g., detect a number of reactions above a threshold),then steps are able to be taken such as asking additional questions,providing statistics, enabling the user to provide a response andautomatically providing a rebuttal of the user's response by factchecking the user's response and providing sources supporting therebuttal, stopping fact checking, and/or providing silly comments suchas instead of presenting “false” as the fact check result, presenting“this is completely wrong, but you always reactive negatively, sonevermind.”

In some embodiments, modifying the fact checking implementation includesutilizing a reverse psychology implementation, and the reaction ismonitored and analyzed. For example, a database stores fact checkresults and corresponding reverse psychology results, and if theconditions are appropriate (e.g., detecting number of negative responsesabove a threshold), then the reverse psychology results are presented.Furthering the example, the reverse psychology results are able to begenerated automatically or manually (e.g., a user reviews a fact checkresult and inputs a corresponding reverse psychology result).

In some embodiments, modifying the fact checking implementation includeschallenging a user when his reaction is negative. For example, the useris requested to provide evidence (e.g., voice input, text) for theuser's position. In some embodiments, the fact checking system isconfigured to receive images, links, text, audio, and/or otherinformation for analysis (e.g., a user is able to drag and drop contentinto a window, upload a file or input the information in another way).The fact checking system then analyzes the user's evidence and providesresults. For example, a user submits a link which disagrees with thefact check result, and the fact checking system fact checks the contentin the link and provides the fact check results to the user (e.g., thisstudy has been refuted by 97% of the scientists in the world) includingcitations which disagree with the content of the link (e.g., pleasereview these links which rebut your link). The citations are able to befound by maintaining a database including the links (or other content)and opposing links (or other content), and retrieving information fromthe database. For example, a website address with opinion X is stored ina database, and a website address with opinion Y (opposing opinion X) isstored in the database with a relationship with the opinion X websiteaddress (e.g., the two cells are linked or otherwise related). In someembodiments, challenging the user includes offering the user a prize ifthe user is able to provide valid support for his position.

In some embodiments, modifying the fact checking implementation includesproviding fact check results determined based on analyzing userinformation (e.g., personality, occupation, political affiliation,psychological analysis). For example, the first fact check result ispresented as usual, and then the user's reaction is analyzed, and if thereaction is negative, the second fact check result is in a differentmanner dependent upon the user information. Furthering the example, theuser's occupation is determined to be Occupation X (e.g., by analyzingsocial networking information or by retrieving it from a database), sothe second fact check result is displayed with specifics of how itaffects the user including citations providing support; however, for asecond user with an occupation of Occupation Z, the second fact checkresult is displayed using reverse psychology. In another example, thesequence of how fact check results are presented is: generic, tailored,tailored with example, tailored with citations and sarcasm forPersonality-type X, and the sequence of how fact check results arepresented is: generic, tailored with example and citations, and no morefact check results for Personality-type Z. In some embodiments, thesequence of providing fact check results in different styles/formats islearned based on the analysis of users' reactions or the user'sreaction. In some embodiments, user information is analyzed anddetermined (e.g., retrieve user occupation from social networking siteor ask user to select their personality type), and the determination isstored (e.g., in cache, metadata, a cookie, a data structure), and eachtime a fact check result is to be presented, the stored information ischecked to determine how to present the fact check result. In someembodiments, a counter is used with the different ways of presentingfact check results to determine which type of result to present (e.g.,present generic result when counter is 0; after detecting negativereaction, increment counter; present tailored result when counter is 1;after detecting negative reaction, increment counter; present tailoredresult using modified sources when counter is 2, and so on).

In some embodiments, users are classified based onopen/closed-mindedness (e.g., a user is queried and/or selects howopen-minded he is, and/or his contacts of a social networking site arequeried). For example, a user with a very open mind is given a score of10, and a very closed mind is given a score of 1. In another example, auser's social networking contacts are queried about how open-minded theuser is, and the social networking contacts each select a value between1 and 10, and then the social networking contacts' selections areaveraged to generate the user's open-mindedness score. And based on theuser's score or classification, different fact check results or factcheck implementations are utilized and/or different fact check sequencesare utilized. For example, a very open-minded user is provided genericfact check results using all sources for fact checking, and a veryclosed-minded user is provided tailored fact check results using onlysources classified as having the same political affiliation as the user.

In some embodiments, modifying the fact checking implementation includesproviding a similar but modified fact check result. For example, a factchecking implementation fact checks a sentence, “President Z is theworst president ever because of A, B, C.” However, A, B and C are false,so the fact check result is “false” or “A, B and C are false.” When theuser's negative response is detected, the fact check result is modifiedto, for example, “President Z is the worst president ever because of J,K and L (which are true); however, A, B and C are false.” Providing asimilar but modified fact check result is able to be performed in anymanner such as by finding a part of a sentence that is factuallyaccurate, opinion, questionable or not factually inaccurate, and addingcontent which corresponds to that part of the sentence instead of or inaddition to the fact check results of the whole sentence or other partsof the sentence. Furthering the example, a relational database is ableto store information such as President Z, and then in related cells, rowor columns, there is information such as items why President Z is greator the greatest, why President Z is terrible or the worst, controversiesof President Z, accolades/accomplishments of President Z, and/or anyother information that is searchable and retrievable. In someembodiments, the relational database is separated into positives andnegatives for each entity, and when content is fact checked, it isdetermined if the content is negative or positive, and information isretrieved from the corresponding positive/negative aspect of therelational database.

In some embodiments, modifying the fact checking implementationincludes: when providing results, only providing the results if a sourcewith the same affiliation as the political affiliation of the user (orother commonality) is utilized to generate (and agrees with) the factcheck result. For example, if a user is a Conservative, fact checkingutilizes only Conservative sources (e.g., sources are able to beclassified manually or automatically based on political affiliation). Insome embodiments, the fact checking is not limited to only sources ofthe same affiliation (e.g., Conservative sources), for example allsources are still used, but the result is only presented if at least oneof the Conservative sources agrees with the result. For example, if only10 Conservative sources are used to fact check an item of content, theresult may be 90% True (e.g., 9 out of 10 find the item to be true).However, if 30 sources are utilized (10 Conservative, 10 Liberal and 10neutral), the result may be 70% False (e.g., 21 out of 30 find the itemto be false). And if at least 1 of the 10 Conservative sources indicatesfalse, then the status of false is presented, and in some embodiments, acite or other indication of the Conservative source with that sameresult is also presented. In another example, 30 sources are used, andall 10 Conservative sources disagree with the result, so a status ofquestionable, unknown, disputed, or other status is presented, or bothstatuses are presented with an indication of which sources were used todetermine which status.

In some embodiments, modifying the fact checking implementation includesproviding fact check results and reactions of other users (e.g., socialnetworking contacts of the user). For example, if a user and 5 contactsread an article, all of their reactions are recorded and provided toeach other. Furthering the example, the user reads the article andreacts negatively as detected by his phone's camera, and after thenegative reaction is detected, information (e.g., a graphic) isdisplayed on the user's phone indicating that 4 contacts reacted thesame way and 1 contact reacted differently. In some embodiments,statistics are provided based on user reactions. For example, 90% ofusers who read this reacted positively and 10% of users reactednegatively. In some embodiments, the other users' reactions are providedto the user, when the user accesses the same web site, article, video,and/or other content. In some embodiments, content that is viewed by oneor more contacts is shared among contacts. In some embodiments,thresholds are used to determine when content is shared. For example, ifa web-story is read by 3 contacts, the story is automatically sharedwith any other contacts of the user.

In some embodiments, modifying the fact checking implementation includesproviding simple information and gradually providing more complexinformation. For example, a database stores fact check resultscorresponding to information, and the fact check results are separatedor ordered based on simple fact check results going to complex factcheck results. In some embodiments, the database is indexed (e.g.,simple fact check result is indexed to 0 or 1, . . . , most complex factcheck result is indexed to 10 or N). Furthering the example, a counteris used, and the counter is increased each time a negative reaction of auser is detected. The counter is used to retrieve the correspondinglyindexed fact check result. In another example, information is factchecked, and the result is false, which is presented as “False.” Theuser reacts negatively, and the counter is increased from 1 to 2 (or 0to 1), and the fact check result is still false (in some embodiments,analysis is not re-done for the same information), but instead of simplystating “false,” a specific segment is indicated as being false, andthen the next time, the specific segment is indicated as being falsebecause of X, and so on, with more and more detail.

In some embodiments, modifying the fact checking implementation includesgradually leading a user toward a desired outcome step by step usingleading information stored in a database (e.g., a database stores all ofthe steps in a process, and each one is provided to a user step by stepleading to a conclusion).

In some embodiments, modifying the fact checking implementation includesoffering perks for receiving fact checking results. In some embodiments,an eye tracking implementation is utilized to track a user's eyes toensure the user reads the fact checking result. If the user does notread the fact check result, a reminder is provided to the user (e.g.,pop up window), or the fact check result is moved to where the user'seyes are determined to be looking.

Modifying the fact checking implementation is able to be implemented onthe same information (e.g., same information is re-fact checked and/orresults are presented in a different manner) and/or differentinformation (e.g., user disagreed with fact check of a first set ofinformation based on fact checking implementation 1, and a second set ofinformation is fact checked using fact checking implementation 2).

As described herein, advertising on a web page is able to be blockedbased on fact checking and/or other events (e.g., user actions). In someembodiments, modifying a web page based on fact check results is able toinclude preventing, blocking or otherwise not displaying anadvertisement. For example, if a web page or web site is determined tohave false or misleading information, the advertisement that wouldtypically be displayed on the web page is not. Preventing theadvertisement from being displayed is able to be implemented either on auser-side or a server-side implementation. For example, an advertisingcompany (or intermediary) does not provide the advertisement to the webpage/site/host. In another example, a user device is given a signal tonot display an advertisement when the user views a specified webpage/site. In some embodiments, the advertisement is displayed, but theweb page or web site (e.g., the owner of the web site) does not receiveadvertisement revenue if the web page/site is determined to have falseinformation. Determining if a web page has false information is able tobe implemented in any manner such as characterizing each phrase,sentence, paragraph or other unit as factually accurate, inaccurate,misleading or another label, and if a quantity and/or percentage of theunits (e.g., sentences) of the web page determined to be factuallyinaccurate (or other specified label) exceeds a threshold, then the webpage is determined to be factually inaccurate, and theadvertising/advertising revenue blockage is implemented. For example, ifa web page is determined to include over 20% (or more than 5 units) offactually inaccurate information, then advertising is blocked for theweb page. In some embodiments, web pages/sites are manually and/orautomatically determined to be factually accurate or inaccurate. Thefactual accuracy label of a web page is able to be embedded in the webpage, in a cookie for the web site, or another location. In someembodiments, web pages/sites that are determined to be factuallyinaccurate are stored in a data structure (e.g., database) which is thencross-checked before displaying an advertisement and/or paying the website. For example, if xyz.com/123.html is determined to be factuallyinaccurate, that link is stored in a database, and when theadvertisement company or intermediary is accessed to display anadvertisement for that page, the advertisement company/intermediarydenies the request, does not count up/count a click, and/or otherwiseprevents any benefit of advertising for the web page with the factuallyinaccurate information. In some embodiments, a mechanism is provided forthe web page/site is able to refute the factually inaccurate claim. Insome embodiments, validity ratings of entities are able to be factoredin when determining whether to display an advertisement/pay ahosting/displaying site. For example, if an author of an article has avalidity rating below a threshold, an advertisement is not displayedwith his article, or payment is delayed until the article/page is factchecked (e.g., manually or automatically) to ensure factual accuracy.

As described herein, a drone device is able to be used to gatherinformation, transmit the gathered information, process the information,fact check information, generate information and/or other tasks. Thedrone device is able to be any device capable of acquiring information.Specifically, the drone device may be a unmanned aerial vehicle orunmanned aircraft system capable of operating without a person in theaircraft such as under remote control which is operated by a human orfully or intermittently autonomously by an external or onboard computer.In some embodiments, the drone device is not a flying device, but rathera water-use device such as a boat or a device capable of moving on land,but again operable either by remote control or autonomously. Asdescribed herein throughout, in some embodiments, the drone deviceoperates under manual control (e.g., by a remote control) orautonomously (e.g., fully or intermittently without manual user input).For example, in some embodiments, a drone device is controlled using aremote control, and in some embodiments, the drone device is able tooperate without any input from the person. The drone device is able tobe employed for journalistic purposes.

In some embodiments, drones are positioned in strategic locations to beable to “arrive at the scene” in an efficient manner. For example,instead of storing a drone at a news network station, multiple dronesare positioned in different locations around a city to provide fasteraccess to newsworthy material. The positioning of the drones is able tobe based on distance from each other, distance to typical hotspots ofactivity (e.g., closer to higher crime locations or closer to higherprofile buildings/landmarks), or any other determination. The drones areable to be stored on rooftops, standalone locations, trees, bridges,and/or any other location. The drones are able to position themselvesbased on analyzed information.

In some embodiments, a drone is configured to monitor police information(e.g., scanners, radios, 911 calls), media information, social networkinformation (e.g., Twitter, Facebook), and/or any other information todetermine a location to acquire information and/or what information toacquire. Monitoring the information is able to include capturing theinformation, processing the information, parsing the information, and/orany other steps to analyze the information to take action. For example,the information is able to be compared with a database of keywords suchas: shooting, terrorist, fire, police, address information, and so on,and when a keyword or a number of keywords above a threshold isdetermined, then the drone moves to the appropriate location. Furtheringthe example, by monitoring 911 calls, the drone detects the word “fire”and a location of “123 Main Street,” so the drone automaticallydetermines a fastest route to the location and moves to a location near123 Main Street. In some embodiments, the drone monitors multiplesources (e.g., police, media, social media), and when a number ofsources above a threshold provide the same or similar information, thedrone moves to the location. In some embodiments, the sources areclassified (e.g., emergency, media, social media), and if a number ofsources in a class above a threshold provide the same or similarinformation, the drone moves to the location. In some embodiments, adrone is able to confirm its position based on comparing rooftop imageswith stored rooftop images. In another example, the drone monitorssocial networks for trending information and/or hashtags, and if thetrending information or hashtag is relevant to news (e.g., #shooting),the drone also determines if an address or other location information isavailable (e.g., in the tweet it says Shots Fired at 123 Main St.#shooting), the drone is able to parse the address information and moveto that location. In some embodiments, information (e.g., socialnetworking information) is cross-checked before moving. For example, thedrone waits until the number of tweets regarding a fire is above athreshold (e.g., 100) before moving, or in some embodiments, the dronestarts moving towards a location after a first threshold (e.g., 50tweets) has been reached, but stops half way (or some other distance) orgoes at a slower speed until a second threshold (e.g., 100 tweets) hasbeen reached. In some embodiments, other devices monitor/listen and thenprovide instructions to the drone. For example, a server device monitorssocial network information, and when an alert is triggered, the serverdevice sends instructions to the drone for the drone to move intoposition.

In some embodiments, monitoring includes searching for visual/audioclues (e.g., detecting with an onboard camera: police lights/car/sirens,fire truck, fire, smoke, weapons, car accident, traffic, explosion, anearthquake, weather events such as lightning or a tornado, sounds suchas gun shots, and/or other triggers). For example, the drone deviceincludes a camera which is able to acquire images/video, and theimages/video are able to be compared with templates (stored in onboardmemory, in the “cloud” or another device). In another example, using asound listening/recording device (e.g., microphone or multiplemicrophones), specific audio (e.g., gun shot, explosion, scream of“help,” car accident, Robocopp alarm, alarm system siren) is able todetected and matched with template audio. Furthering the example, a gunshot sound is able to be acquired and compared with template audio whichstores different gun shot sounds. In some embodiments, visualconfirmation of where the gun shot occurred is determined. In someembodiments, location implementations (e.g., triangulationimplementations) are utilized to determine where the audio is comingfrom. In some embodiments, multiple drones are utilized to determine thelocation of the source the audio, for example, differences in how longit takes the sound to reach each drone are used to determine thelocation of the source of the audio. For example, the sound is receivedat the North microphone slightly before the West, East and Southmicrophones, so the drone knows the sound came from the North. In someembodiments, by having multiple sound recording devices on the drone(e.g., on opposite ends of the drone device), a single drone is able todetermine a direction or location of the source of the audio. In someembodiments, the audio information is analyzed (onboard or by anotherdevice) to determine additional information (e.g, type of gun shot, typeof explosion, gender of voice, words spoken). When a visual/audio clueis determined/detected, the drone is able to send location information(e.g., GPS coordinates, address information) to a remote location (e.g.,news station). Additional information acquired by the drone (e.g., usingadditional onboard devices such as a thermometer, barometer, clock,calendar, altimeter, GPS device, accelerometer, gyroscope) is able to beacquired and sent as well such as time, date, traffic conditions,weather conditions, and more.

In some embodiments, the drone is triggered by an alarm system signal(e.g., Building X sends a signal to a drone or drones because ofpotential criminal activity). The alarm system is able to broadcast,send a direct signal and/or communicate in any other way to one or moredrones. For example, a neighborhood is able to utilize a single drone ormultiple drones which serve any house with a corresponding securitysystem. Furthering the example, a drone is centrally positioned within aneighborhood of 100 houses which each have a security system. The 100security systems are able to communicate with the drone such that eachhouse does not need its own drone.

In some embodiments, the information captured by the drone isautomatically deleted within a designated amount of time and/or is neverstored, unless triggered to store the information. For example, forprivacy purposes, although the drone may roam to monitor one or moreproperties, the drone only stores data that is captured when a triggeris detected (e.g., a security system detects a breach or the dronedetects suspicious activity as determined by template comparisons). Bynot storing the non-triggered data, people's privacy is bettermaintained as there is no/less stored data to hack and steal.

The information monitored/acquired is processed onboard or by anotherdevice (e.g., the Cloud). Processing as described herein is able to beinclude parsing, converting (e.g., from sound, video, image to text),storing, modifying, merging, and/or any other processing.

In some embodiments, the information (e.g., the processed acquiredinformation) is used for fact checking as described herein. In someembodiments, information is fact checked using drone-acquiredinformation in combination with the social networking implementations asdescribed, or any form of fact checking. For example, if breaking newsis reported (e.g., via social networking or another news source), theinformation acquired by the drone(s) is used to fact check the breakingnews and indicate any misinformation. In some embodiments, theinformation is used to fact check other drone information. For example,if drone 1 acquires a picture of a car accident, and indicates that car1 hit car 2, but drone 2 acquires other pictures of the car accidentwhich clarifies that car 2 hit car 1. In some embodiments, thedrone-acquired information is fact checked using other sourceinformation.

In some embodiments, additional actions are taken using the acquiredinformation such as automatically generating a story (e.g., video clip,images, article), automatically summarizing the story, broadcastingvideo live, transmitting content (e.g., video, audio, image) to a newsorganization, and/or other actions.

Automatically generating a story is able to be performed in any manner.For example, a video clip or image acquired by the drone is able to beposted to social media such as Facebook® or Twitter®, and theappropriate hashtags are automatically assigned based on comparing theacquired content with templates. In some embodiments, a user (e.g.,host) is able to add audio or visual commentary to the drone videoremotely (e.g., the audio while talking in the news room is added to thedrone's video). In another example, after detecting an incident (e.g.,car accident), the drone device takes one or more pictures, and poststhe pictures via a social network with a short description automaticallygenerated based on analyzing the pictures. Furthering the example, a caraccident is detected on I-5 at 8:30 a. The drone takes a zoomed inpicture to show the damage of the cars with the text “accident betweentwo vehicles at 8:30 a on I-5.” Based on comparisons with car accidenttemplates involving two vehicles, the text “accident between twovehicles at” is generated, “8:30 a” is filled in based on the clockdevice on the drone, and I-5 is filled in based on GPS coordinatesand/or image comparison of the Interstate. The drone takes a secondpicture which is zoomed out to show traffic. The text “traffic buildingup on I-5” which is based on a comparison of the second picture with atemplate of traffic and the GPS/mapping information determined by thedrone device, is able to be provided with the second picture on thesocial network. Thus, an up-to-date story of the traffic accident isable to be provided via social networking or any other news outlet usingthe drone with or without any user involvement. The story is able to beupdated automatically based on time (e.g., updated every 5 minutes) orbased on significant events (e.g., detecting changes in the scene suchas detecting a police car or tow truck arriving or traffic lessening).The automatic content generation is able to be implemented without userintervention.

In some embodiments, the drone automatically generates content inresponse to breaking news. For example, the drone detects and analyzes atweet or other breaking news information, and responds by acquiringinformation related to the breaking news. For example, if a tweetincludes the text, “explosion in downtown City X, 5 buildings burning,”a nearest drone to downtown City X navigates to the target area andacquires content (e.g., video, images, audio). The drone is able totransmit (e.g., tweet) the content, segments of the content, and/orgenerated content. Furthering the example, the drone takes a picture ofthe buildings on fire and tweets the picture with text “4 buildingsburning” by analyzing the image and locating 4 separate fires or 4separate buildings with fire.

In an example of generating a story, the drone takes a picture of thesky or horizon, takes a temperature and humidity reading using sensors,and sends the information to be dispersed (e.g., sends a tweet with thepicture, temperature and humidity information or updates a Facebook®page with the same information).

In some embodiments, the drone sends images/videos/audio and/oradditional information (e.g., a generated story or generatedinformation) to a person to generate a story or supplement the story.

In some embodiments, the drone utilizes object recognition, actionrecognition, facial recognition, heat (e.g., body heat) recognition,and/or any other recognition. As described herein, object recognitionincludes recognizing police vehicles, fire vehicles, fires, and/or anyother object. In an example of action recognition, the drone is able tomatch/detect specific actions such as shooting, running, throwing,polluting, illegal dumping, littering, any criminal activities,beneficial activities, and/or any other actions. In an example of facialrecognition or person recognition, the drone locates and matches asuspected criminal (e.g., with a database), and sends a tweet, “suspectspotted at First St. and Main St.” In some embodiments, the informationis sent to the appropriate authorities (e.g., the police). In someembodiments, an action is taken by the drone based on the recognition(e.g., starting to record video, following the object such as followinga police car with its lights on). The recognition is able to beperformed in any manner such as acquiring an image/video, comparing theimage/video with a database of templates and determining if any matchesare detected. In some embodiments, the image or video comparisoninvolves point-by-point matching, block matching, estimated matching,and/or any other matching implementations.

In some embodiments, the drone implements object tracking. For example,after an object is recognized/located, the drone continues to track theobject until the drone is triggered to stop tracking the object. Thetrigger is able to be any trigger such as a timeout (e.g., track for 5minutes then stop), a signal from another device (e.g., studio sends asignal to stop tracking), moving out of range, or detecting a change(e.g., suspect apprehended) or lack of change (e.g., suspect notmoving). In some embodiments, multiple drones are used to cover certainranges or zones, and each drone stays within its zone. The drones areable to coordinate with each other to ensure a target is not lost. Forexample, if a suspect moves from zone 1 to zone 2, then a second droneis waiting in zone 2 (at the border of zone 2 and zone 1) where thesuspect is headed (e.g., based on the first drone's location/trajectory)to continue tracking the target.

In an example of a tracking implementation, a drone receives detailsregarding a location of a suspect, so the drone goes to the location,and using recognition technologies detects the suspect. Once the suspectis acquired, the drone continues to track the suspect by using therecognition technologies to continue to track the correct suspect. Insome embodiments, the drone is able to digitally tag the suspect totrack the suspect in addition to or instead of using recognition. Insome embodiments, the drone captures and parses specific details of thesuspect (e.g., shirt color, hat color, height) and continues to trackbased on the acquired details. In some embodiments, the drone is able toanticipate actions of the suspect by analyzing the surroundings. Forexample, if the suspect is approaching a high wall, the drone is able toanticipate the suspect will be turning right or left, or if the dronedetects or knows the police are approaching from the left, the suspectwill likely go to the right. The drone is then able to take actions inadvance such as flying to the right before the suspect moves to theright to ensure being able to continue to track the suspect. Similarly,if the suspect goes in a tunnel or under coverage, the drone has or isable to access data of what is not visible such as a map including theexit of the tunnel or all exits of a building. In some embodiments, thedrone is able to be used to guide the police toward the suspect orforce/coax the suspect to the police. In some embodiments, the drone isable to toggle between different detection devices such as a regularcamera when the suspect is visible and an infra-red camera when thesuspect is obscured. In some embodiments, when a drone detects asuspect, the drone is able to communicate with other drones to assist.In some embodiments, requesting assistance depends on the details of thesuspect, for example, if the suspect has a criminality rating above athreshold such as an armed murder suspect (e.g., based on informationreceived from the police), then multiple drones are used to track thesuspect from different angles. In some embodiments, the drone is anested drone implementation as described herein which is capable ofseparating when desired to be able to be in multiple places at once.

Automatically summarizing a story is able to implemented in any manner.For example, after the drone captures content, the drone or anotherdevice, summarizes the content by selecting parts of the content anddiscarding parts of the content. Summarization is able to includeextraction, abstraction or any other form of summarization.

In some embodiments, image summarization includes capturing a set ofimages from a video to represent the video. For example, a picture fromevery 10 seconds of a video is shown.

In some embodiments, summarization includes video summarization such asgenerating vines or other short video clips (e.g., a trailer). Thegenerated video summaries are able to be stored and/or transmitted inchronological order, relevance, importance, excitement, rating, and/oranother order. Relevance is able to be determined in any manner such asbased on a keyword comparison of the summarized video and other content(e.g., text of the summarized video and text of the other content arecompared, and the higher number of matching keywords, the more relevantthe summarized video is). Importance is able to be determined in anymanner such as a keyword comparison of a database containing importanttopics (e.g., as determined by users, the media). Excitement is able tobe determined in any manner such as based on trending information (e.g.,comparing keywords of the summarized video with currently trendingtopics). Rating is able to be determined in any manner such as based onthumbs up/down. In some embodiments, the summaries are generated usingrelevance to order aspects of the video. For example, a drone captures10 minutes of footage, and then the drone or another device (e.g.,server) summarizes the video in 30 seconds of footage with the mostrelevant 5 second clip first, followed by the second most relevant clipand so on, or the most exciting clip first and so on.

In some embodiments, “dead” or boring time is deleted or separated froma video to shorten or summarize the video. For example, a drone capturesvideo of the scene of an accident. At time 00:00-03:00, the video showsthe cars in a stationary crashed position. At time 03:01-05:00, thevideo shows the cars in the crashed position, and a police officer hasarrived. At 05:01-15:00, an ambulance has arrived as well. At time15:01, the police and ambulance leave the scene. Instead of showing afull 15+ minutes of video, the video is able to be summarized byincluding 10 seconds starting at 00:00, then 10 seconds starting at03:01, then 10 seconds starting at 05:01, and 10 seconds starting at15:01, which shows the car crash, the police arriving, the ambulancearriving, and the ambulance leaving. Any scene changes are able to bedetected and captured to generate the summary. For example, every tenthframe of a video is able to be compared with the previous frame, and ifthere is a change in the current frame above a threshold, then thatimage/video (e.g., a few seconds before and/or after) is included in thesummary. In another example, if the change in the current frame is notabove a threshold, then the video is skipped or deleted. The frame isable to be compared using any image/video comparison algorithm (e.g., bypixel-by-pixel comparison or group of blocks comparison or superpixelcomparison). In some embodiments, the focus of the frame is able to benarrowed to a portion of the scene. For example, if a traffic accidentoccurs on a busy road, the outer edges of a frame may be changing oftenbased on moving traffic, but that may not be relevant to the accident,so the traffic is ignored (e.g., by having a focus box on the caraccident vehicles). The focus is able to be determined by patternmatching or template matching as described herein and generating aborder around the matched scene. In some embodiments, a video summaryshows fast forwarding through boring or repetitive moments, so the useris able to briefly see what is going on while skipping to the moreinteresting moments.

In some embodiments, suspicious, exciting or important activity isextracted from a video to generate a summarized video. For example,using template matching (e.g., comparing an image/frame with a databaseof templates which represent suspicious, exciting or importantinformation), an important moment is detected in a video, and 10 secondsbefore and 10 seconds after the important moment are captured and usedas a summary or part of a summary.

In some embodiments, image/video analysis is utilized to classifydifferent aspects of the video for labeling and summarizing the video.For example, using template matching: cars, people, streets, policevehicles, ambulances and other items are labeled in a video, and thelabels are utilized to summarize the video. For example, a datastructure stores the labels and the time in the video when they appearor disappear from the video, and based on the data in the datastructure, a summary is generated. For example, a text summary is ableto indicate: two vehicles involved in accident from 00:00-30:00, policeon scene 02:30-11:46, and ambulance on scene 5:34-11:46. The templatematching or any other image/video analysis is also able to be used todetect and label items/actions such as blood, fighting, shooting, a gun,a knife, and/or a fire. In some embodiments, detection is optimized bynarrowing down options based on a first category detection and thenusing sub-category items/actions within the first category. For example,if a car accident scene is detected, sub-category items/actions todetect include cars, blood, police, ambulances, street signs, but do notinclude airplanes or knives. This optimizes the search and detection ofitems since these items/actions are more likely to be found at the sceneof an accident. In some embodiments, if an item is not detected in thesub-category, then categories outside of the sub-category are used. Forexample, if one of the people pulls out a gun in road rage, the gun oraction may not be detected using the sub-category items/actions, but bygoing to outside categories after checking the appropriate sub-category,the item/action is detected (e.g., the gun). In some embodiments, goingto extra categories involves going to related categories first beforegoing to unrelated categories (e.g., police is related to fire) to againoptimize the search and detection process.

In some embodiments, the summary only includes still images with orwithout an accompanying text description.

In some embodiments, a summary is generated by detecting keywords in thevideo and clipping a video starting with a predetermined time before andending at a predetermined time after the keyword. For example, a videois acquired of a crime scene, and the audio is analyzed to determinewhen the keyword “shooting” is detected, and a summary which starts 10seconds before the word “shooting” and 10 seconds after iscaptured/generated.

In some embodiments, generating a summary involves converting videoand/or audio (or a portion of the video/audio) to text (e.g., a tweet).In some embodiments, the tweet includes a link to the video/audio.

In some embodiments, generating a summary includes focusing on sendingthe summary through social networking. For example, a drone capturescontent, and then the captured content is sent to user devices throughTwitter®, Facebook®, YouTube® or any other social networking system orweb page. In some embodiments, the drone-captured content is sent to aserver or other device, and then the server or other device sends thedrone-captured social networking content. In some embodiments, asdescribed herein only portions or summaries of the content are sent viasocial media, for example, a captured image from the drone video (or thedrone takes pictures) is sent every minute via Twitter® with the samehashtag for each one. In another example, when a highlight isdetermined/detected, an image or short video clip is sent via socialnetworking. A highlight is able to be determined or detected usingkeyword comparison, image comparison or any other manner. For example,if a drone video captures audio, and the audio is converted into text,the text is searched, and when a keyword is detected, a video clipand/or an image is captured/extracted and provided on social media. Insome embodiments, video before and/or after the keyword is detected isalso captured and distributed. In some embodiments, multiple imagesbefore and after the keyword is detected are captured and distributed(e.g., 3 images spaced 5 seconds apart, before and/or after the keyword,are distributed). Similarly, using image comparisons, when a desiredimage match is detected (e.g., match of traffic increasing), then animage is captured and sent via social media. In some embodiments,keywords and/or images are arranged in a hierarchy such that there aregroups and sub-groups to optimize searching/comparison. For example,using image comparison, an accident is detected, then under accidentitems such as police, ambulance, people are searched for and detected,and items that would not likely be found at an accident such as floodwater will not be searched for.

In some embodiments, the drone or another device is able to providesupplemental information to generate a narrative or summary. Forexample, in addition to providing video clips, images, audio, and/or atext summary, the drone or other device acquires information fromanother source (e.g., the Cloud) to add information. For example, thedrone analyzes a license plate of a vehicle that is in a car chaseavoiding the police, and using the Cloud, the drone indicates that thecar is registered to Jane Doe. In another example, when a shooting isdetected, the drone and/or other device analyzes police records andprovides the supplemental information that this was the fifth shootingof the year in this city.

The information acquired by the drone is able to be broadcast as livevideo. For example, a drone acquires video and transmits it to a newsbroadcasting company which then transmits it for display on television.In some embodiments, the drone broadcasts the video directly to users.Similarly, the drone is able to transmit content for streaming video(e.g., on YouTube®).

In some embodiments, a drone (or another device such as a server) isutilized for providing and/or blocking advertising. For example, thedrone determines a location based on GPS information, and provides anadvertisement or additional information for the location. Furthering theexample, the drone is hovering near a hotel, and based on GPSinformation, street information, image recognition or another analysis,the hotel is identified as XYZ Hotel, so an advertisement for XYZ Hotelis displayed along with the drone-acquired information (e.g., in apicture-in-picture or split screen view). In some embodiments, thecontext of what the drone is capturing is taken into account whendetermining the advertisement. For example, if the drone is recordingnews of a murder in front of XYZ Hotel, it would be in bad taste to showthe advertisement of the hotel, and the hotel would not want to beadvertising a nearby murder, so either the XYZ Hotel advertisement wouldnot be shown and/or a different advertisement would be shown (e.g., anadvertisement for an upcoming gun show). The context is able to bedetermined in any manner, automatically and/or manually, for example, byimage/video template matching and/or any other image/video analysis,converting police information from audio to text, or by receiving textinformation from another source describing the situation (e.g., policereport).

FIG. 19 illustrates a diagram of an exemplary drone for fact checkingand content generation according to some embodiments. The drone 1900includes a body 1902 and one or more propellers 1904. In someembodiments, another lift mechanism is used such as forced air below thebody. Coupled to the body 1902 are additional components such as a oneor more video cameras 1906 and one or more microphones 1914. The one ormore video cameras 1906 are able to be any type of video camera such asa regular video camera, a wide lens video camera, an infrared/thermalvideo camera, a night vision camera, a 360 degree video camera, apanoramic camera, a digital camera and/or any other type of camera/videocamera. In some embodiments, two way cameras (e.g., a camera with lensespointing in multiple and/or opposite directions) or multiple cameraspointing in opposite directions are utilized, so the drone does not haveto turn around. In some embodiments, when multiple cameras are utilized,the cameras operate independently including zooming at different amount(e.g., zoom in with one camera and zoom out with another camera toprovide varying views of a scene). The one or more video cameras 1906are able to be positioned anywhere on the drone 1900 such as an arm, thecentral component, or elsewhere. In some embodiments, in addition to orinstead of a video camera, one or more photo cameras are included and/orthe video cameras have photo capture capabilities. The drone 1900includes one or more sensors/components 1908 such as a temperaturesensor, barometer, altimeter, wind speed detector/sensor, light sensor,moisture sensor, magnet, magnetic sensor, location mechanism (e.g., GPSdevice) and/or any other devices for measuring, sensing and/or detectinginformation. In some embodiments, the sensors are able to be used fornavigation. For example, using the temperature sensor, the drone is ableto detect that the air is warmer in a specific direction, and continuesin that direction to track a fire. Similarly, a light sensor (e.g.,capable of detecting different wavelengths of color) is able to be usedto track an object emitting light (e.g., using a light sensor thatdetects specific light colors such as blue for police) to detect and/ortrack a police vehicle. In some embodiments, the drone 1900 includescomputing components 1910 such as a memory/storage for storing factchecking summarization and content generation applications, a processorfor processing commands/data, a networking/transmitting component forsending data to other devices, and/or any other computing components.The drone 1900 includes other standard drone components such asgyroscopes, accelerometers, a power supply, and/or any other components.In some embodiments, the drone 1900 includes solar panels/cells/devices1912 or other alternative energy power sources such as a gyroscope orwind turbine to acquire/provide power. The solar panels 1912 are able tobe positioned anywhere such as on the arms of the drone 1900, thecentral component, and/or elsewhere. The drone 1900 is able to includeone or more microphones 1914. In some embodiments, the microphones 1914are part of the video cameras 1906, and in some embodiments, themicrophones 1914 are a separate and/or additional component. Themicrophones 1914 are able to be positioned anywhere on the drone 1900.In some embodiments, the drone 1900 includes a spot light or anotherlighting system. In some embodiments, the drone 1900 includes protectivegear such as armor or shielding. In some embodiments, the drone 1900includes camouflaging and/or a cloaking mechanism. The cloakingmechanism is able to be implemented in any manner such as a video screenon the bottom of the drone 1900 and a video camera pointed above thedrone 1900, so the video screen is able to display the sky above thedrone 1900 such that the drone does not appear to be in the sky.Although one configuration of a drone is shown in FIG. 19, anyconfiguration of the drone is able to be implemented (e.g., with more orfewer propellers, a different body shape/configuration).

In some embodiments, the drone 1900 includes components 1916 such as alight, a laser, a tracking mechanism (e.g., to track another object, orfor another object to track the drone such as a control room to be ableto visualize where the drone is on a computer-generated map), a gimbal,gyroscope and/or accelerometer for stabilization, and/or an obstacleavoidance mechanism (e.g., using camera and depth map, or laser, orsound to determine if objects are near and how close they are). Forexample, the drone 1900 uses laser tracking (e.g., is capable ofdetecting a laser light and following it) and/or includes a laser forpointing at objects. In some embodiments, the drone 1900 includesinterchangeable parts such as lenses which are able to be replaced whilethe drone 1900 is in operation or not. For example, an extra lens isstored within the body of the drone, and if the first lens is damaged orcovered (e.g., by water), then the drone drops or removes the first lensand replaces it with the extra lens. The lens is able to be attached viaa clipping mechanism and easily removed via the clipping mechanism, sothe change occurs while in use.

In another example, the drone 1900 includes one or more lights whichchange color and/or flash based on what is detected (e.g., by thecamera, microphone, light sensor and/or other component) and/or based onany other condition/information. For example, if the video cameradetects police lights and/or the microphone detects a police siren, thenthe one or more lights illuminate blue; if a fire is detected or firetruck lights are detected, then the one or more lights illuminate red;if ambulance lights are detected, then the one or more lights illuminatewhite; and if a dangerous situation is detected such as detecting a gun,then the one or more lights flash yellow.

In some embodiments, the drone 1900 utilizes remote wireless charging.For example, the drone is able to charge its power supply by flying to acharging station which is able to be on a roof, tower, pole, and/or anyother location. The drone 1900 is able to determine the nearestavailable charging station. For example, each charging station hasdetection implementation (e.g., a weight sensor or electricallydetermining a connection) which is connected to a database and/or systemwhich tracks the charging stations and is able to relay the informationto the drone 1900. Furthering the example, a drone is looking to charge,and it accesses the charging station information to determine that thenearest charging station is occupied, so the drone goes to the secondnearest charging station. In some embodiments, the amount a drone ischarged is also stored and accessible by other drones (e.g., if drone 2is 99% charged, it may be preferable for drone 1 to go to that chargingstation instead of a further one since drone 2 will be charged soon).The amount of charge, time remaining to charge and/or other informationis able to be determined and calculated in any manner. For example, if abattery is 50% charged and each 1% of charge takes 1 minute, then thebattery will take another 50 minutes to charge. In some embodiments, acharging station has a standby location to wait to charge or to waitafter charging has been completed, so that two drones are able to be atthe charging station. In some embodiments, the drones are able to knowthe location of other drones to determine if another drone is en routeto a charging location to avoid traveling to a location only to find itoccupied. As described herein, the drones are able to be tracked, andthe tracking information is stored and accessible by other drones anddevices. The drones are able to calculate estimated times of arrivals,current battery levels, estimated time to charge, and/or any otherinformation to charge efficiently. In some embodiments, a drone has adedicated charging station. In some embodiments, a charging station isable to charge multiple drones at the same time.

In some embodiments, traffic, crime and/or other factors are analyzed todetermine where the drones are positioned. For example, if a newsstation has 5 drones, the drones are positioned closer to where moretraffic issues, crime and/or other events occur as determined byanalyzing traffic patterns and/or crime statistics. The analysis is ableto include adjusting based on time of day and/or time of year. Forexample, at rush hour, two drones are positioned near an interstatewhich typically has traffic issues during rush hour, but then at night,the two drones are positioned closer to a high crime area of the city.

In some embodiments, the drone 1900 includes foldable/collapsiblepropellers capable of folding when the drone 1900 is not in use. Thedrone body is also capable of folding/collapsing in some embodiments fora smaller storage footprint. In some embodiments, the drone 1900 foldsinto an aesthetically pleasing form for storage. For example, the drone1900 is colored with green leaves on the bottom such that when it foldsup, the green leaves are visible, and the drone appears to be a tree orbush. The drone 1900 is able to fold up to appear to be any object suchas a fruit, a plant, an animal, a building material (e.g., gargoyle,chimney), sports item, and/or anything else. In some embodiments,instead of folding up to look like another object, the drone 1900 ismerely shaped and colored to look like another object.

In some embodiments, the drone includes a protective device to protectthe drone from the weather. For example, the drone includes an umbrellawhich is retractable and is able to be stored when not in use.Furthering the example, in the middle of the drone is an open space forstoring the umbrella, and when activated, the umbrella opens like astandard umbrella providing coverage for the drone. Components are ableto include protective heaters/coolers such as a defroster for a cameralens. In some embodiments, the drone includes waterproofing includingensuring tight seams and using wax or other items to keep water awayfrom electrical components. The blades/propellers are able to bedesigned/configured to create an air flow to protect from rain/snow. Forexample, an additional set of propellers are configured to cause a mildupward airflow which would push the rain/snow around the drone. To keepthe drone at a stable position, the upward airflow is met with acompensating downward airflow.

FIG. 20 illustrates a diagram of multiple drones according to someembodiments. As described herein, the multiple drones 2000, 2002, 2004are able to communicate with each other and/or with a server 2006. Thedrones 2000, 2002, 2004 are able to receive and/or send informationfrom/to the server 2006 and each other. The drones 2000, 2002, 2004 areable to be positioned and/or configured to capture multiple angles ofinformation (e.g., from a first side, from a second side opposite thefirst side). The drones 2000, 2002, 2004 are able to be used to performanalysis such as detecting where a sound came from (e.g., by determiningwhich drone receives the sound first to triangulate the location of thesource). Any number of drones is able to be used (1 to any number), andthe drones are able to be any type of drone (e.g., the drones do not allhave to be the same brand/style/configuration although they could be).The drones are able to communicate with any other device (e.g., aserver, an end user device, the Cloud, a Facebook® server, a Twitter®server) and are able to communicate through any device (e.g., cellularphone tower or satellite). The drones are able to communicate directlyor indirectly to devices. The drones are able to use any form ofcommunication or combination of communications (e.g., WiFi, cellular).

In some embodiments, multiple drones are used to perform the functionsdescribed herein (e.g., monitor, acquire, fact check, transmit,generate). In some embodiments, the multiple drones fact check eachother's information, or the information acquired from the drones iscombined and compared to do a composite fact check. For example, twodrones acquire information, and that information is combined to besource information to fact check a breaking news report. The time/dateinformation of each of the streams of information from the drones iscompared/utilized to properly compare the information. For example,information from Drone 1 with a timestamp of 10 p is compared withinformation from Drone 2 with a timestamp of 10 p or a timestamp withina certain range.

In some embodiments, one or more drones are utilized to acquireinformation about a target from multiple angles. For example, a dronevideo records a target from a first angle, and then the drone videorecords the target from a second angle, and so on. In some embodiments,the drone records for a period of time (e.g., 2 minutes) at each angle.In some embodiments, the drone records, and then based on an externalinput (e.g., command from a control room), the drone moves to adifferent position to record from a different angle. In someembodiments, the drone records and determines that it is recording afirst side, then after a period of time, the drone records a secondside, and so on, to capture all four sides of the target. In someembodiments, the drone factors in the position of the sun, shadows, andother visual effects/artifacts to determine the appropriate angle. Forexample, if the sun is causing bright flashes such that it is difficultto view the target, the drone adjusts the angle such that the suneffects are minimized. Minimizing the sun effects can be implemented inany manner such as moving until a camera is able to visually detect aminimized amount, or based on knowing the current position of the sunbased on the time of day and a proper location for minimizing the suneffects. In some embodiments, the drone determines if there is anyobstruction as described herein and repositions itself or another droneto reduce/minimize the amount of obstruction. In some embodiments, oneor more drones move closer to a target to better acquire audio and/orvideo. For example, a first drone acquires video from farther away, anda second drone acquires video from a closer position to acquire betteraudio.

In some embodiments, using a geolocation or other position determiningimplementation, the multiple drones are able to determine which drone isnearest to a desired/target location. For example, if Drone 1 is 1 mileaway from a scene of an accident, but Drone 2 is 100 feet away from thescene of the accident, Drone 2 is activated and goes to the scene. Thelocation of the target/scene is able to be determined in any manner suchas the drone being sent information from a server or the drone acquiringthe information while monitoring information. For example, the dronesare monitoring police radio information, and they all detect that anaccident occurred at 1^(st) Street and Main Street, and based on mappinginformation stored in or accessible by the drones, the drones are ableto determine which is closest to the accident. In some embodiments, thedrones know the location of each other, and in some embodiments, eachdrone sends information to the other drones after determining thelocation of the accident. For example, each drone sends its respectivedistance to the others, and only the drone with the shortest distancegoes to the accident. In some embodiments, additional factors areconsidered such as remaining battery life, speeds of the drones (e.g.,if one drone is able to fly faster than another, then distance is notthe only factor), camera/feature capabilities (e.g., if one drone has abetter camera, it may go instead of or in addition to another closerdrone, for example, if the distance difference is below a threshold,then the drone with the better camera goes) and/or any other factors.

In some embodiments, a drone or other device determines if anincident/scene should have more drones to capture different views (e.g.,huge multi-building fire—yes, but small fire—no), where the nearestdrones are, and/or if they will likely make it to the scene in time. Forexample, if a second drone is 1 mile away and travels at 10 mph, thefire just started and is estimated to last 2 hours, so yes, the seconddrone is sent, but a third drone that is 30 miles away is not sent.Determining if another drone will make it in time is able to beimplemented in any manner such as by classifying an event anddetermining at what point it is in the event. For example, a first dronedetects a fire, and then also detects the size of the fire, and based onthe size of the fire, an estimated duration of the fire is determined(e.g., 1 building=1 hr, 2 buildings=3 hrs). No fire trucks detected yetcould also add another hour. The estimated time until the end of theevent can change as well (e.g., upon the arrival of 5 fire trucks, theestimated end goes down by an amount).

In some embodiments, based on one or more of the components (e.g., videocamera, microphone, sensor) the drone automatically adjusts itslocation/position. For example, if the drone detects that the currenttemperature has exceeded a threshold temperature due to an approachingforest fire, the drone increases its altitude or moves further away fromthe fire. In another example, by tracking the forest fire with thevideo, the drone is able to calculate how quickly the forest fire ismoving toward the drone, and the drone is able to take actionpreemptively. Furthering the example, if the forest fire is moving at apace of 1 mile per hour, and the drone is one quarter mile away from theedge of the fire, the drone automatically moves away from the fire atthe same pace as the fire (e.g., 1 mile per hour) or the droneautomatically moves in less than 15 minutes.

When the drone detects an obstructed view, the drone is able toautomatically reposition itself to avoid the obstruction. Detecting anobstructed view is able to be implemented in any manner such asdetermining a target (e.g., person of interest, cars of an accident,building on fire), and if another object comes in front of that object,then the view is considered obstructed. The target is able to bedetermined automatically (based on the monitored and parsed information,it is determined that the house at 123 Main St. is the target or basedon image/video analysis—the house on fire is the target based ontemplate matching) and/or manually (e.g., user in control room selectshouse to be the target). The depths of each object are able to bedetermined (e.g., using a depth map), and if the depth of an obstructionis closer/less than the desired target, then the view is obstructed. Insome embodiments, a full shape of an object is determined, and if lessthan the full shape is detected, then the object is obstructed. In someembodiments, some obstruction is able to be tolerated (e.g., telephonewires in front of a house on fire or one small tree in front of a forestfire). An obstruction threshold is able to be set/used to determine ifan obstruction is detected. For example, an obstruction percent iscalculated (e.g., how much is the house obstructed by the telephonewires). The obstruction percent is able to be calculated by determininghow much of a total object (e.g., person, house) is able to be viewedand how much is obstructed by another object (e.g., obs %=obs amt/totalamt, 1%= 1/100). The amounts are able to be calculated based on pixels,groups of pixels, or any other unit. A total object is able to bedetermined using templates, previous unobstructed views (e.g., adatabase of images of houses), extrapolation or any otherimplementation. If the obstruction percent is greater than theobstruction threshold (e.g., obstruction threshold is 25%, and thecalculated obstruction percent is 30%), then the drone automaticallyrepositions itself to minimize the obstruction percent (e.g., at leastget the obstruction percent below the obstruction threshold or to find alowest obstruction percent). In some embodiments, the drone retains theobstruction values from previous locations, so as to not revisit alocation (unless it has been determined that the obstruction is amovable object (e.g., car). The drone compares the obstructionpercentages and searches for an obstruction percentage below thethreshold or for the lowest obstruction percentage. If the drone cannotfind a position with an obstruction percentage below the obstructionthreshold, the position with the lowest obstruction percentage is used.In some embodiments, projected obstruction percentages are calculated todetermine where to move to (e.g., based on a captured scene, it isdetermined there are no telephone wires on the East side of the house,and there are wires on the West side, so instead of trying the West sideof the house, the drone moves to the East side to calculate theobstruction percentage).

In some embodiments, the drone is able to automatically determine whento acquire video. In some embodiments, the drone continuously acquiresvideo. In some embodiments, the drone acquires video only after itdetermines a target has been detected. For example, if a burglarysuspect is being tracked, the drone does not record video until asuspect is detected (e.g., a person matching a reported description).Similarly, if a fire has been reported, the drone does not record untila fire is detected by the drone (e.g., based on temperature readingsand/or by detecting fire in the using the video camera). A remote useris able to trigger the drone to start recording.

In some embodiments, the drone links with one or more other videosystems. For example, the drone receives one or more videos, and factchecks the one or more videos using the drone-acquired content(video/images/audio). In some embodiments, the video from one videosource is spliced/merged/incorporated with the content captured by thedrone. In some embodiments, video content and drone content aredisplayed in a split-screen format.

FIG. 21 illustrates a diagram of a nested drone implementation accordingto some embodiments. The nested drone 2100 includes a primary drone 2102and one or more propellers 2104 or other flying mechanism on the primarydrone 2102. Coupled to the primary drone 2102 are additional componentssuch as a one or more video cameras 2106 and one or more microphones2114. The one or more video cameras 2106 are able to be any type ofvideo camera such as a regular video camera, a wide lens video camera,an infrared/thermal video camera, a night vision camera, a 360 degreevideo camera, a panoramic camera, a digital camera or any other type ofcamera/video camera. In some embodiments, two way cameras (e.g., acamera with lenses pointing in multiple and/or opposite directions) orcameras pointing in opposite directions are utilized, so the drone doesnot have to turn around. The one or more video cameras 2106 are able tobe positioned anywhere on the primary drone 2102 such as an arm, thecentral component, or elsewhere. In some embodiments, in addition to orinstead of a video camera, one or more photo cameras are included and/orthe video cameras have photo capture capabilities. The primary drone2102 includes one or more sensors/components as described in FIG. 19such as a temperature sensor, barometer, light sensor, altimeter, windspeed detector/sensor, moisture sensor, magnet, magnetic sensor,location mechanism (e.g., GPS device) and/or any other devices formeasuring, sensing and/or detecting information. In some embodiments,the primary drone 2102 includes computing components as described inFIG. 19 such as a memory/storage for storing fact checking and contentgeneration applications, a processor for processing commands/data, anetworking/transmitting component for sending data to other devices,and/or any other computing components. The primary drone 2102 includesother standard drone components such as gyroscopes, accelerometers, apower supply, and/or any other components. In some embodiments, theprimary drone 2102 includes solar panels/cells/devices as described inFIG. 19 or other alternative energy power sources such as a gyroscope orwind turbine to acquire/provide power. The solar panels are able to bepositioned anywhere such as on the arms of the primary drone 2102, thecentral component, and/or elsewhere. The primary drone 2102 is able toinclude separate one or more microphones 2114. In some embodiments, themicrophones 2114 are part of the video cameras 2106, and in someembodiments, the microphones 2114 are a separate and/or additionalcomponent. The microphones 2114 are able to be positioned anywhere onthe primary drone 2102. In some embodiments, the nested drone 2100includes a spot light or another lighting system. In some embodiments,the nested drone 2100 includes protective gear such as armor orshielding. In some embodiments, the nested drone 2100 includescamouflaging and/or a cloaking mechanism. The cloaking mechanism is ableto be implemented in any manner such as a video screen on the bottom ofthe nested drone 2100 and a video camera pointed above the nested drone2100, so the video screen is able to display the sky above the nesteddrone 2100 such that the drone does not appear to be in the sky. Thenested drone 2100 includes a secondary drone 2150. The secondary drone2150 includes a body and one or more propellers or other flyingmechanism. In some embodiments, the secondary drone 2150 includes any ofthe components described for the primary drone 2102 such as one or morevideo cameras, microphones, power supplies, computing components,sensors, and/or any other components. The secondary drone 2150 is ableto couple to the primary drone 2102 of the nested drone 2100 in anymanner. For example, in some embodiments, the secondary drone 2150 sitsatop the central component of the primary drone 2102. In someembodiments, the primary drone 2102 includes a receiving aperture forreceiving the body of the secondary drone 2150. In some embodiments, alocking mechanism is utilized to secure the secondary drone 2150 withthe primary drone 2102. The secondary drone 2150 is capable ofseparating from the primary drone 2102 such that two separate drones arecapable of flying and acquiring information. In some embodiments, thedrones communicate with each other and/or communicate with other devices(e.g., a server or remote control). For example, when multiple suspectsare spotted and they separate, the secondary drone 2150 is able todisengage from the primary drone 2102 and track one suspect, while theprimary drone 2102 tracks the other suspect. Tracking is able to beimplemented in any manner as described herein, such as by acquiring animage, shape, color, grouping of pixels or any other trackingimplementation which the drone determines to be worth following, andthen maintaining that tracking implementation within the view (e.g.,where the video camera is still capturing video of the person/object) ofvideo camera of the drone, including searching for the trackingimplementation if the tracking implementation is not within the videocamera. For example, if a drone detects a person with a red shirt, thedrone follows the red shirt. Any image processing technique is able tobe used to distinguish the person or object being tracked. In someembodiments, the primary drone 2102 and the secondary drone 2150 areconfigured to share power when together so that when they separate, theyhave equal power (e.g., the primary drone 2102 uses power from its powersupply for a period of time and then the primary drone 2102 uses powerfrom the secondary drone's power supply for a period of time). In someembodiments, the secondary drone 2150 is inactive when coupled to theprimary drone 2102, and in some embodiments, the secondary drone 2150 isactive when coupled to the primary drone 2102. In some embodiments, thesecondary drone 2150 only activates when desired or needed (e.g., theprimary drone 2102 is having issues with its propellers or power orthere is a desire/need for extra lift). The secondary drone 2150 is ableto be activated manually or automatically (e.g., based on sensorsdetecting the nested drone 2100 losing altitude, the secondary drone2150 activates its propellers). Although one configuration of a drone isshown in FIG. 21, any configuration of the drone is able to beimplemented (e.g., with more or fewer propellers, a different bodyshape/configuration, a different nesting configuration such as nestingfrom the bottom instead of the top). In some embodiments, the separatedrones are able to be manually controlled separately, automaticallycontrolled separately or a combination thereof.

FIG. 22 illustrates a diagram of the primary drone and the secondarydrone of the nested drone separated. As described herein, the primarydrone 2012 and the secondary drone 2150 are able to separate and be usedseparately to perform actions such as acquire information.

FIG. 23 illustrates a flowchart of a method of fact checking informationusing drone information according to some embodiments. In the step 2300,information is monitored. As described herein, the drone or anotherdevice is able to monitor police information by receiving a police radiosignal, social networking information, news information, mediainformation and/or any other information. The monitoring is able to beperformed in any manner as described herein. In the step 2302, theinformation is processed. For example, the information is converted,formatted, parsed and/or any other processing occurs. In the step 2304,the information is fact checked using information captured by the drone.For example, the drone captures information which is used as sourceinformation to compare with the monitored/processed information. In someembodiments, the drone determines what information to capture based onthe monitored information. For example, if the drone monitors a policeradio, and processes the statement “shooting at 105 5^(th) street,” thenthe drone navigates to that location and acquires information to comparewith any details reported regarding the shooting at that location.Furthering the example, a news report says 10 people have died, butbased on a picture and/or video, the number of bodies not moving is ableto be counted automatically using the drone and/or another device toverify or disprove the report. In some embodiments, the drone-acquireddata is combined with other data (e.g., a report that three people havedied at the hospital. In some embodiments, fact checking includesproviding real-time data in response to a story/report. In the step2306, a status of the information is provided. Providing the status isable to be performed in any manner as described herein such as bysending fact checking results, summary information, live information,and/or any other information acquired by the drone to users via socialmedia, a direct transmission, a broadcast, and/or any otherimplementation. In some embodiments, fewer or additional steps areimplemented. In some embodiments, the order of the steps is modified.

FIG. 24 illustrates a flowchart of a method of utilizing a drone tocapture and provide information according to some embodiments. In thestep 2400, one or more drones are operated. The drones are able to beoperated manually, automatically (e.g., without user involvement) or acombination thereof. Operating the drones is able to include any of theoperations described herein such as navigating the drones to anappropriate location, separating a nested drone, and/or communicatingwith another drone to come to the location or go to a differentlocation. In the step 2402, the one or more drones acquire information.Acquiring the information is able to be performed in any manner and isable to include any information acquisition such as using cameras,sensors or other devices to acquire information. Acquiring informationis also able to include receiving information from a server or otherdevice (e.g., monitoring tweets). In the step 2404, one or more actionsare taken with the information. For example, the information acquired bythe one or more drones is: live streamed to viewers, summarized,processed, used to generate additional content, used to take a secondaryaction such as requesting another drone, used to take pictures,transmitted (or parts of the information are transmitted) to otherdevices, used to fact check other information, and/or any other actions.In some embodiments, fewer or additional steps are implemented. In someembodiments, the order of the steps is modified.

In some embodiments, the drone flies in any manner and in any direction.For example, for the drone to get from Point A to Point B, the droneflies up to a specified height and then flies directly from Point A toPoint B. In some embodiments, designated flight paths are generated andutilized by the drone. For example, an invisible road system isgenerated and utilized by the drone to navigate. Furthering the example,a map that parallels the streets on land (or similar or a modifiedversion optimized for the sky) is stored in the drone or in a server,and the drone uses that map to navigate. In some embodiments, additionalelectronic devices are positioned to help the drone navigate. Forexample, cellular phone towers are used to help establish pathways.Furthering the example, a drone follows a direct line parallel to adirect line between cellular phone towers. In some embodiments, thedrone accesses information of do not fly zones (e.g., airports,government buildings) to avoid flying where it is not supposed to go.The do not fly zones are also able to be factored in when calculatingtravel time for the drone to a location.

In some embodiments, the drone implements privacy protections. Forexample, the drone stores or accesses a database of user locations thatdo not allow pictures or videos to be taken. In another example, thedrone automatically blurs/blocks any faces or other objects when sendinga picture or video to be displayed publicly. Furthering the example, thedrone recognizes a user's face and then blurs the located face area. Aperson's voice is also able to be distorted using a voice distortionimplementation to maintain their vocal privacy. Entire people are ableto be blurred out or blocked out. In some embodiments, people or facesare replaced by markers (e.g., icons). In some embodiments, a displaydisplays markers for all living items. For example, if the view is adistant view, to assist viewers in recognizing what they are viewing,icons are displayed to represent people, animals and/or any otherobject. The icons are able to be representative (e.g., police badge forpolice officers, plus sign for medical workers and fireman helmet forfiremen, fire symbol for fire, gun for shooting).

In some embodiments, the drone is able to provide multiple feeds ofcontent. For example, as described herein, the drone is able to acquirecontent using a plurality of cameras. The content is able to bedisplayed to users in a split-screen format, picture-in-picture formator any other format. If one camera includes zoomed out information, theicons are able to help users visualize what is being shown.

In some embodiments, the drone-acquired content utilizes/incorporatesvirtual reality technology. For example, the drone acquires contentusing a camera capable of producing virtual reality content, or thecamera acquires content, and then the content is converted to be virtualreality content. By providing virtual reality content, viewers are ableto be immersed in the news. In some embodiments, fact checking resultsare displayed in the virtual reality content. For example, if breakingnews is fact checked using drone-acquired content, the drone-acquiredcontent is displayed side-by-side with the breaking news.

In some embodiments, the drone utilizes augmented reality. For example,additional content is displayed to appear relative to what is seen bythe drone. For example, if the drone captures video of a forest fire,the augmented reality is able to add names of relevant landmarks.Furthering the example, a forest fire in Yosemite is recorded using adrone, and text labels are assigned to indicate Half Dome and otherlandmarks/features, to give viewers a better understanding of whereexactly the forest fire is. In some embodiments, measurements and/orother information is displayed on the augmented reality to provide evenmore information. The measurement information is able to be determinedusing mapping information and/or any other analysis. For example,mapping information includes distance information from point A to pointB, so that information is able to be displayed in an augmented realitydisplay.

In some embodiments, the drone includes additional features such as acontainer for storing and dispensing water or compressed CO₂ or otherchemicals (e.g., to extinguish a fire).

In some embodiments, the drone synchronizes with one or more additionaldevices. For example, the drone synchronizes with equipment on theground such as a microphone or video camera. Furthering the example, thedrone detects that a television station is interviewing witnesses of acrime, and the drone synchronizes the video and/or audio captured fromthe drone with the video and/or audio of the person being interviewed(e.g., both are transmitted/displayed at the same time). Thesynchronization is able to be based on time, sound detection and/or anyother implementation.

In some embodiments, the drone or a device communicating with the dronedetermines which information to provide to the public and whichinformation to keep secret or hold for review (e.g., classifyinginformation). Classifying information is able to be implemented in anymanner such as comparing images with templates of content, and if animage matches a template of secret content, then the information isconsidered secret content. For example, dead bodies or excessive bloodare considered information that is generally not acceptable for publicdisplay on a news broadcast, so if such information is detected, thenthe content is kept secret. In some embodiments, a user (e.g., networkemployee) is able to override the secretive aspect to enable the contentto be displayed.

Drones and social networks are able to work together to enhance newscoverage. In addition to using social networks to select sourceinformation, social networks are also able to be used to determine whichdrone to use, where the drone information is sent to and/or otheraspects. For example, the location of mobile devices (e.g.,phones/tablets) is able to be known (e.g., many mobile devices allowother devices to know their location), and the location of the mobiledevices is able to be used to determine which drone is utilized toacquire information. Furthering the example, if several users send out atweet that has the hashtag, #GoldenGateParkShooting, then the droneassigned to Golden Gate Park is utilized to go to the scene and acquirereal-time information. In addition to or instead of the hashtag, actualuser device location information is able to be acquired and analyzed todetermine which drone to utilize and/or where the drone should go. Inanother example, a user has an accident and sends a message either tothe police or to someone else. Embedded in that message or accessiblebased on that message is the location information (e.g., GPSinformation) of the user's device, and a drone (possibly the nearestdrone) goes to that location to acquire additional information.

In some embodiments, since some news is local, the drone sends contentto a limited set of users (e.g., based on location, based on anotherquality/feature/characteristic). For example, if a Chicago-specificevent occurs, then the drone-acquired content is only sent to mobiledevices in Chicago. Determining which devices the content is sent to isable to be implemented by the drone or another device (e.g., server).How the content is sent to the limited set of users is able to beimplemented in any manner. For example, the drone may taglocation-specific content, the content may be sent to a specific socialnetworking group, the drone may use NFC, GPS or other locationimplementations to send the content to devices within a specified range,or the content may only go to mobile devices with a designated area code(e.g., only 415 and 650 numbers). In some embodiments, a drone ismatched with users based on location (e.g., users in a specifiedlocation may be prompted to join Drone X's newsfeed or social network).In some embodiments, keywords in drone content and/or user content areused to match the drone with users. For example, a message is sent toall users who have used hashtag #xyz to join Drone X's newsfeed orsocial network.

In some embodiments, one or more drones are preemptively positioned. Forexample, a drone goes to where traffic typically builds up based onhistorical data. Furthering the example, if traffic typically builds uparound 5 p, the drone leaves so that it arrives to acquire video of thetraffic at 5 p (or slightly before 5 p).

FIG. 25 illustrates a diagram of a drone security system according tosome embodiments. The drone security system 2500 includes a securitysystem 2502 and a drone 2504. The security system 2502 is able toinclude any security system component such as one or more video cameras2506, a control unit, an alarm, one or more sensors (e.g., magneticstrips which detect when a window or door is opened), one or more motionsensors, one or more audio detectors (e.g., glass break detector) and/orany other security system components. Any drone is able to be used asdescribed herein. In some embodiments, the drone 2504 includes a storagecompartment/shooting mechanism 2508 for storing an object/substance 2510(e.g., a net, paint, pepper spray, a tracking device, water). Forexample, the storage compartment 2508 includes one or more doors whichopen to drop the object 2510, or the storage compartment 2508 is able toshoot out the object 2510. The tracking device is able to be any devicewhich emits a signal or is otherwise trackable. The tracking device isable to include an adhesive mechanism (e.g., tape, magnet) to adhere toa person or the person's vehicle. For example, the drone 2504 drops theobject 2510 which is a tracking device on the hood or top of a vehicle,and the object 2510 adheres to the vehicle using a magnet.

The drone 2504 and/or the security system 2502 detect a person, abreach, and/or any other trigger. In some embodiments, the drone 2504and/or the security system 2502 communicate the detection, breach and/orother trigger to each other and/or other devices. For example, thesecurity system 2502 detects a breach at a window and sends asignal/information to the drone 2504 that the window has been breached.In some embodiments, the trigger is a doorbell being pushed, athermostat/fire alarm/CO2 alarm trigger, an internet of things devicecommunicating a trigger, motion sensor trigger, a video detectiontrigger, and/or any other trigger (where the trigger is able be anytrigger such as a audible or silent signal sent to the drone 2504 and/orsecurity system 2502). In some embodiments, the drone 2504 and/or thesecurity system 2502 take an action after detecting a trigger. Forexample, after the security system 2502 detects a door has been opened,the security system 2502 sends a signal and/or data (e.g., identifyingwhich door has been opened), the drone 2504 is activated and flies tovideo record and/or take pictures of the incident. In some embodiments,the drone waits a period of time (e.g., 10 seconds) to see if the userdisarms the security system. In some embodiments, the drone 2504 locatesa suspect using object recognition and/or based on where the triggeroccurred. For example, the drone 2504 goes to the front door where abreach was detected. The drone 2504 video records and analyzes the videoin real-time to detect a person and determines a burglar is leavingthrough the front door. Unlike fixed video cameras, the drone 2504 isable to fly around to multiple locations to locate the burglar and alsois able to track and follow the burglar. The drone 2504 is able torecord and transmit location information to the appropriatepeople/entities. The drone 2504 is able to continuously video record andtake pictures of the suspect as well. The drone 2504 is able to shine aspot light on the suspect, tag the suspect (e.g., with paint that thedrone stores in a storage container, such as a small compartment on thebottom of the drone which opens to drop paint or a paint ball, or with atracking device which sticks to the suspect and/or the suspect'svehicle), and/or take any other action. The storage compartment is ableto include one or more doors/panels which open to release/shoot anobject/substance. In some embodiments, the drone 2504 takes pictures ofspecific targets such as the suspect, the suspect's car, accomplicesand/or any other targets. For example, the drone stores or accesses adatabase of template targets, and then searches for items that match thetemplate targets, and if there is a match, then a picture and/or videois acquired of the target. The drone 2504 is able to focus the pictureson specific aspects of the target, such as a suspect's face or themake/model of a vehicle and the license plate. In some embodiments, thedrone 2504 takes multiple pictures from multiple angles. In someembodiments, the drone 2504 (or another device using content acquired bythe drone) is able to access police records and/or other information tocompare the content acquired to determine further information. Forexample, using facial recognition and a police database, the drone 2504is able to identify a known felon which increases the alert status(e.g., send more police officers). In another example, the drone 2504captures a license plate number and by comparing that number with astolen vehicle database, the drone 2504 is able to report the vehicle asa known stolen vehicle. The drone 2504 is also able to use imageprocessing techniques (e.g., a depth map and object size calculationanalysis) and objects nearby to generate helpful information such as asuspect's height and/or weight. For example, the drone measures variousobjects of a property (or receives the measurement information) such astrees, bushes, statues, doors, roof height, garage door height, vehicleheight, deck height, fence height, and/or any other information, andthen uses that information to compare with a suspect acquired in apicture to determine the suspect's height.

In some embodiments, the drone 2504 and/or other cameras (e.g., one ormore security cameras stationed on the property) acquire pictures/videosof the property which are considered base/control pictures/videos (e.g.,they are taken at a time when it is known that nothing harmful ishappening). In some embodiments, it is ensured that the doors andwindows are closed before taking the base/control pictures/videos. Then,the drone 2504 and/or other cameras acquire pictures/videos of theproperty later on (e.g., while the security system is armed or when atrigger is detected) to compare with the base/control pictures, and ifthere is a change, the drone 2504 and/or security system 2502 take anaction (e.g., sound an alarm, contact a user such as send the newpicture/video or the differences between the pictures/videos to theuser). For example, the similarities of the images are grayed/blackedout and the differences are highlighted or colored. In some embodiments,several base/control pictures/videos are acquired of each location(e.g., from different angles, at different times of the day (e.g., houselooks very different in day light versus nighttime), at different timesof the year (e.g., a yard in the summer may look very different than inthe winter), under different weather conditions (e.g., sunny, raining,cloudy, snowing). Then, when comparing a current picture/video with thebase/control pictures/videos, the current picture/video is compared withthe base/control pictures/videos at the appropriate angle, weathercondition and so on. In some embodiments, the current picture/video iscompared with all base/control pictures/videos, and in some embodiments,the process is optimized by utilizing known or determined information(e.g., based on the current date, the weather report/onboard sensors,the clock, position information). For example, the drone clock indicatesthat it is 11:00 am; thus, there is no reason to compare the currentpicture with a nighttime picture. Similarly, if a temperature sensor onthe drone reads that it is 100 degrees outside, a base picture in winteris not used for comparison. In some embodiments, the analysis determinesif any base/control pictures/videos are a match with the currentpicture/video, and so as long as one is a match, there is no actiontriggered. In some embodiments, the most appropriate base/controlpicture/video is selected (e.g., by finding the base/controlpicture/video that matches the most criteria (time/date, weather,angle/location)) and compared with the current picture/video, and ifthere is a match using any image processing comparison implementation,then no action is taken; otherwise an action is taken. Based on time ofday and/or any other criteria, pictures/videos are able to account forother base/control changes such as an owner's vehicle is in the drivewayfrom 6 p to 6 a, on M-F, but is not there from 6 a-6 p, thus there is noreason to trigger an alert that the car is missing when the car issimply at the user's work. Additional information/factors are able to beaccounted for such as vacations, holidays, a vehicle not returning homeat exactly at 6 p because of traffic and/or staying late at work, and/orany other information/factors. The additional factors are able to bedetermined in any manner such as cross-checking with a user's calendar,traffic information, work schedule, and/or any other accessible data.The base/control pictures/videos are able to be stored locally (e.g., onthe drone, the security system, a local computer) and/or remotely (e.g.,in the cloud). The base/control pictures/videos are able to be updatedautomatically and/or manually to ensure any changes to the property areaccounted for (e.g., when a user adds a new decoration near the frontdoor, this should not trigger a police call). In some embodiments, whenthe system detects a new feature (e.g., decoration near front door), theimage is sent to the user, and the user is able to confirm or deny thatthe new feature is acceptable and should be included in base/controlpictures/videos.

In another example, the security system 2502 and/or the drone 2504contact the police, the owner of the house, the security company and/oranother person/entity. In some embodiments, the drone 2504 issynchronized with the one or more video cameras 2506 (e.g., the drone oranother device is able to access and/or analyze the information in theone or more video cameras 2506; for example, the drone determines that asuspect is visible in video camera 1, so the drone navigates to thatlocation). In some embodiments, the drone 2504 and/or security system2502 send a video, images, a text summary of what is being recorded, animage/video summary as described herein, location information and/or anyother data to the police and/or another user/entity (e.g., an incidentreport is sent as a tweet or to a group email). The drone 2504 is ableto generate a description (e.g., police report) of an incident. Forexample, by detecting a breach of a house and an alarm going off, thereport indicates a burglary, and a description of a blue shirt, blackhat, 6 foot, male, running West is indicated. The color detection and/orobject detection are able to be performed using any image processingtechniques. In some embodiments, the data acquired and transmitted bythe drone 2504 is encrypted. For example, the data stored on the drone2504 is encrypted and/or the data that is sent from the drone 2504 toanother device (e.g., a user's smart phone) is encrypted before it issent and then decrypted by the receiving device to protect againstsomeone hacking the system/communication.

In some embodiments, incident pictures/videos are captured and storedfor insurance purposes (e.g., sent to homeowner's insurance company forthem to assess damages). In some embodiments, live video from the droneis streamed to the user and/or other users (e.g., police, media).

In some embodiments, the drone 2504 receives information from one ormore other drones/security systems (e.g., a neighbor's security systemdetected a breach, or a neighbor's drone detected suspicious activityand notifies the drone 2504 and/or drones in a network, and based on thereceived information, the drone 2504 (and/or other drones) take action(e.g., begin to patrol the area/property). Specific details from theother drone are able to be communicated, so the drone 2504 is able tomonitor for a specific event (e.g., look for a tall man with a grayjacket).

In some embodiments, the drone 2504 waits on the roof, in a tree, on aplatform, or another on-site location (inside or outside; hidden orvisible). In some embodiments, the drone 2504 roams (e.g., flies aroundand/or hovers). In some embodiments, the drone 2504 determines when toroam based on analyzing social networking information (e.g., members ona neighborhood social networking group have reported burglaries in theneighborhood of the drone/security system including where and when theburglaries occur, and the drone detects keywords/other information inthe social networking information), police information, historical data,a user-specified time, and/or any other data. For example, if mostburglaries occur between 10 a and 11 a and 9 p and 11 p, then the drone2504 roams the property during those times. In some embodiments, thedrone roams when the security system 2502 is armed or when a droneroaming period is set. In some embodiments, the drone 2504 roams basedon real-time information (e.g., someone on social media reports aburglary in the neighborhood, so the drone 2504 begins to roam).

The flight path of the drone 2504 is able to be executed in a variety ofways. The flight path is able to be a direct flight path (e.g., thedrone 2504 goes directly from point A to point B). For roaming, theflight of the drone 2504 is able to a set pattern (e.g., go around thehouse in the same direction) or a variable pattern (e.g., clockwise thencounterclockwise, and then randomly going one or the other). In someembodiments, the drone 2504 roams by going to certain points around thehouse (e.g., garage, then front door, then roof for a 360 degree view,and so on). In some embodiments, a user is able to program the route ofthe drone 2504 for roaming. In some embodiments, a random seed generatoris used to determine the random roaming path of the drone 2504.

In some embodiments, the drone 2504 enters the house (or otherstructure/building) or waits outside the house. For example, the drone2504 waits near the entry point or trigger point of the house/alarm. Insome embodiments, the drone 2504 is a nested drone as described herein.By using a nested drone, the drone is able to acquire two sets ofinformation (e.g., one drone goes inside and the other waits outside;one drone goes to the front of the house, the other goes to the back;one drone tracks a first suspect, the other drone tracks a secondsuspect; one drone tracks a suspect and the other drone looks forvehicles and/or other evidence by comparing scanned information withtemplates of bullets, weapons, tools, vehicles or other items). In someembodiments, the drone 2504 is configured to stop the suspect (e.g., bydropping a net stored in a compartment in the body of the drone 2504) orblind the suspect by shining a bright light in the suspect's eyes orspraying pepper spray at the suspect. As described herein, the drone2504 is able to utilize lights on the drone 2504 to indicate differentemergencies (e.g., flashing blue for a burglary, flashing red and bluewhen a gun is detected). The drone 2504 is also able to include audiblealerts (e.g., using onboard speakers) such as a siren, a recordedmessage, and/or any other audio. Using the speakers and/or other alerts,the drone 2504 is able to announce the details of an incident/crime(e.g., burglary at Location X, two burglars, clothing information, bodyinformation). For example, the drone 2504 determines the type of crimebased on detail matching (e.g., breach of security system equalsburglary, or video/image matching showing broken/open door or windowequals burglary, and the drone is able to acquire images/videos of thesuspects and using image/video analysis is able to determine items suchas hair color, height, skin color, vehicle/license plate information,and direction traveling, and other tools are able to acquire additionalinformation such as GPS coordinates), and the acquired/determinedinformation is able to be announced through the speakers as the dronefollows a suspect or moves around (e.g., around a neighborhood) toannounce the incident/crime. In some embodiments, the drone 2504 and/orsecurity system communicates to nearby devices (e.g., using NFC, WiFi,or any other communication) to provide an alert. For example, the drone2504 communicates via WiFi to neighbors' mobile phones (e.g., textmessage or social networking) that a burglary just occurred at House X,and details are provided (e.g., address, suspect details, direction ofthe suspect traveling, vehicle information, items stolen). In someembodiments, multiple drones and/or nested drones are able to be usedwith the security system 2502.

In some embodiments, the drone 2504 and/or security system 2502 are usedfor fact checking purposes as described herein.

FIG. 26 illustrates a diagram of a drone with shielding according tosome embodiments. The drone 2600 is able to be any of the dronesdescribed herein including any of the components described herein suchas propellers, video cameras, microphones, circuitry, to name a few. Thedrone 2600 includes shielding 2602 such as a kevlar plate (or anotherprotective material). In some embodiments, the shielding 2602 isbulletproof, and in some embodiments the shielding 2602 is not. Theshielding 2602 is able to be any size. For example, the shielding 2602is able to be sized to protect the entire drone from an attack frombelow or to protect the battery/motor. Multiple shields are able to beused to protect specific components such as the cameras and/or themicrophones. In some embodiments, the shield 2602 is a lighter materialto merely hide components. For example, the shield 2602 is a thin sheetof material (e.g., cloth, plastic, metal such as aluminum foil) whichprevents someone on the ground from seeing the varying components of thedrone. In some embodiments, the shield 2602 is see-through such asbullet proof glass/plastic. In some embodiments, the shield 2602 hasholes and/or is positioned so that the camera devices and/or otherdevices are able to perform without interference from the shield 2602.For example, the cameras are able to be positioned around the shield (orto acquire content around the shield) or such that if the drone 2600tilts slightly, the drone 2600 is able to acquire video without theshield in the way. In some embodiments, the shield 2602 is retractable.For example, the shield 2602 is positioned on the side of the drone 2600or closer to the belly/body of the drone 2600 when not in use, and thenwhen placed in secure mode (e.g., triggered automatically by detectingwater, gun fire or another trigger or by a manual trigger by a userusing a remote device), the shield 2602 extends into position.Furthering the example, the shield 2602 is directly underneath the bodyof the drone 2600 (e.g., with a minimal gap between the body and theshield such as 1 cm), and then when triggered, the shield 2602 extendsdown a few inches (e.g., 2 or more inches) which protects the drone 2600and may block the view of the cameras. In some embodiments, the shield2602 includes one or more extendible wings 2604 which spreadhorizontally to further protect the drone 2600. For example, the wings2604 are within the shield 2602 and extend out from within or the wings2604 fold out from the bottom of the shield. Any other implementationsto ensure the shield 2602 does not interfere with other functions of thedrone 2600 are able to be utilized. In some embodiments, the dronecomponents (e.g., body, propellers, camera covers) are made of shieldingmaterial (e.g., bullet proof material). In some embodiments, the shield2602 and/or drone 2600 is painted to blend in with surroundings such asthe sky. In some embodiments, the shield 2602 and/or drone 2600 arepainted in different colors such as the shield 2602 to resemble the skywhen looked at from below and the drone body painted the same color asthe roof so people may not notice the drone while stationary. The topand bottom of the drone 2600 are able to be painted different colors.

Any of the aspects of the drone are able to be implemented manually,semi-automatically, or automatically, and any combination of these areable to be implemented. For example, control of the drone is manual, butacquisition of content is automatic or vice versa, or both areautomatic.

In some embodiments, the drone device and/or security system areutilized to ensure a package, vehicle, furniture (e.g., television),jewelry and/or any other object are not removed from a property (e.g.,the home and/or surrounding yard).

FIG. 27 illustrates a flowchart of a method of utilizing the droneand/or security system to prevent an object from being removed from alocation according to some embodiments. In the step 2700, an object isrecognized/detected by the security system and/or a drone device (e.g.,a single or a nested drone). For example, a package is placed on thedoorstep of a house, and the security system (e.g., security camera)and/or the drone device detects the object. Detecting the object is ableto be implemented in any manner such as detecting an RFID chip (or othersimilar technology such as Near Field Communication, Bluetooth®) in/on adelivery box (e.g., the security system, drone and/or other device hasan RFID reader for detecting the RFID chip); object recognition via acamera of the security system and/or drone (e.g., the camera detectsbox-shaped, brown objects or any other shaped or colored object usingany content recognition implementation); receiving a deliverynotification from the delivery/shipping company (e.g., UPS®/USPS/FedEx®)or the merchant (e.g., Amazon.com®)—for example, when a delivery man forUPS® drops off a package, he scans the package and indicates that thepackage has been delivered, that notification goes to a UPS® serverwhich is then able to relay that information to any otherInternet-connected device such as the security system or the dronedevice, and therefore, the security system and/or the drone device areaware that a package has been delivered on the property; receiving adirect delivery notification from the delivery company device to thesecurity system/drone device (e.g., package scanner sends a signal tothe security system), and/or any other detection technique. In someembodiments, the security system and/or drone communicate with one ormore devices/servers (e.g., user, delivery company, merchant server) tolet them know that the package is protected by a security system. Insome embodiments, messages (e.g., text, email, social networkinginformation) are monitored for indications of packages being deliveredor will be delivered at a certain time (e.g., friend sends text message“I just dropped off your package” or “I'm dropping off a package at 5p”), which trigger the security system and/or drone to monitor thepackage (or to monitor the package at the specified time), propertyand/or search for the package if not detected (e.g., package is out ofview of security camera, but the drone moves/hovers to another locationand locates the package). Detecting/recognizing specific people such asmailmen or delivery men (e.g., recognize UPS® logo) triggers themonitoring and/or searching for the object. In some embodiments, thedoorbell is coupled to the security system, and when the doorbell isrung, the security system/drone locates the package to begin monitoringthe package and/or surroundings. In some embodiments, it is detected ifthe door is opened, which would indicate the package has been retrievedby the user. In some embodiments, the door includes a tactile sensorwhich is able to communicate with the security system to triggermonitoring for the package.

In some embodiments, the object detected is a vehicle in front of thehouse, and if the vehicle is not recognized as a user vehicle (e.g., bycontent recognition), then the time the vehicle leaves and otherinformation (e.g., duration, images of the vehicle) are acquired and/ortransmitted to the user's device (e.g., smart phone). The vehicledetected could be a delivery vehicle which is also able to be used totrigger monitoring the delivered package.

In the step 2702, the object is monitored. Monitoring the object is ableto be implemented in any manner such as tracking any movement of orchanges to the object with the security system and/or the drone device.In some embodiments, the drone device moves to monitor the object and/orhovers above the property. For example, after a package is delivered,the drone device re-positions itself so that the package is within theview of the camera of the drone device and/or the drone is able todetect a person entering the property to give an advanced warning (e.g.,based on facial recognition, “you are not recognized, please leave thepremises”). In another example, the drone device re-positions itself onthe package or hovers over or near the package, so that any would-bethief would avoid attempting to steal the package. Monitoring is alsoable to include using the drone in an aerial position to detect changesbased on snapshots or videos of the property at different times.Monitoring is able to include image/video processing techniques such asgenerating a depth map of the object and scene after the object ispositioned (e.g., delivered), and periodically measuring the depth toensure the depth of the object does not change, and if the depth mapchanges, then a signal is sent. For example, cameras from the securitysystem and/or drone continuously acquire video/images, and a depth mapis generated for each image/frame (or specified frames such as a frameper 10 seconds) which includes the depth of the package (or the depth ofthe package is able to be determined from the depth map), and eachdepth/depth map is compared to determine the distance of the package (orchange of distance), and the distance of the package (or change ofdistance) is able to be compared with a threshold. Using the depth mapand/or other image processing techniques, it is able to be determined ifthe package is moving or moves off the property or into the house. Forexample, the depth map includes the distance to the property line and isable to determine that the package has crossed the property threshold.In another example, image processing recognizes the package goingthrough an open door and/or the depth map confirms the distance of theopen door. In some embodiments, a scanner device (e.g., RFID, NFC) isable to detect if the package (tag/chip) has moved (e.g., out of range).Image processing/content recognition techniques are able to be used todetect a person/object (e.g., robot, external drone) near the package,and if the person/object is not recognized or if the security system hasnot been disabled, then an action is taken. Image processing/contentrecognition techniques are able to be used to detect actions ofpeople/object such as damaging the package by recognizing actions and/oraudio recognition.

In the step 2704, an action is taken based on the object (e.g., moving,being damaged, view of the object is blocked). For example, if the alarmsystem has not been disarmed (or when disarmed), and the package isaffected (e.g., moves more than a specified distance such as 1 foot),then an action/notification (e.g., message is sent to user's smart phonethat package is moving) and/or alarm is triggered. The user is then ableto disarm the “package moving” alarm. Disarming the alarm systemindicates that the package is able to be moved. In some embodiments,there are separate disarm features (e.g., disarm package does not disarmthe rest of the security system). In some embodiments, the action takenis the drone device moving the package to a secure location such as onthe roof or to the back yard. In some embodiments, a warning is givenbefore the alarm is triggered (e.g., a few beeps or a voice messagealerting that an alarm will sound if the person does not leave). In someembodiments, the drone follows the package. In some embodiments, feweror additional steps are implemented. In some embodiments, the order ofthe steps is modified. The method is able to be utilized with existingobjects (e.g., to prevent an existing statue or lawn ornament from beingremoved or interior items such as a television). The existing objectsare able to be recognized automatically (e.g., by video camera contentacquisition) or input manually into a database (e.g., input serialnumber or description). In some embodiments, to prevent a drone frombeing used to steal a package, anti-drone measures are able to beimplemented such as a device to grab/catch a drone (e.g., paint, net),an EMP to send a small, electric pulse to disable the drone, a device todisrupt air to crash the drone, or the user's drone to attack/follow theinvading drone.

FIG. 28 illustrates a diagram of devices for securing an object at alocation according to some embodiments. One or more trigger devices 2800communicate with one or more servers 2802 which communicate with the oneor more security devices 2804. In some embodiments, the trigger device2800 is able to communicate directly with the security devices 2804. Thetrigger device 2800 is able to be the delivery scanner (e.g., bar codereader) or any other device configured for triggering an indication thata package has been delivered. The security system or drone could be thetrigger device 2800 and/or the security device 2804. The servers 2802are able to be user servers, delivery company servers, merchant servers,security company servers and/or any other servers/devices or acombination thereof. The security devices 2804 include the securitysystem, the drone and/or any other security devices. The trigger devices2800, the servers 2802 and the security devices 2804 communicate todetect an object, monitor the object and take an action based on theobject (e.g., movement of the object).

In some embodiments, the social networking fact checking system is asmartphone application including, but not limited to, an iPhone®, Droid®or Blackberry® application. In some embodiments, a broadcaster performsthe fact checking. In some embodiments, a user's television performs thefact checking. In some embodiments, a user's mobile device performs thefact checking and causes (e.g., sends) the results to be displayed onthe user's television and/or another device. In some embodiments, thetelevision sends the fact checking result to a smart phone.

Utilizing the social networking fact checking system, method and devicedepends on the implementation to some extent. In some implementations, atelevision broadcast uses fact checking to fact check what is said orshown to the viewers, and a mobile application, in some embodiments,uses fact checking to ensure a user provides factually correctinformation. Other examples include where web pages or social networkingcontent (e.g., tweet or Facebook® page) are processed, fact checked, anda result is provided. The fact checking is able to be implementedwithout user intervention. For example, if a user is watching a newsprogram, the fact checking is able to automatically occur and presentthe appropriate information. In some embodiments, users are able todisable the fact checking if desired. Similarly, if a user implementsfact checking on his mobile application, the fact checking occursautomatically. For a news company, the fact checking is also able to beimplemented automatically, so that once installed and/or configured, thenews company does not need take any additional steps to utilize the factchecking. In some embodiments, the news company is able to takeadditional steps such as adding sources. In some embodiments, newscompanies are able to disable the fact checking, and in someembodiments, news companies are not able to disable the fact checking toavoid tampering and manipulation of data. In some embodiments, one ormore aspects of the fact checking are performed manually.

In operation, the social networking fact checking system, method anddevice enable information to be fact checked in real-time andautomatically (e.g., without user intervention). The monitoring,processing, fact checking and providing of status are each able to occurautomatically, without user intervention. Results of the fact checkingare able to be presented nearly instantaneously, so that viewers of theinformation are able to be sure they are receiving accurate and truthfulinformation. Additionally, the fact checking is able to clarify meaning,tone, context and/or other elements of a comment to assist a user orviewer. By utilizing the speed and breadth of knowledge that comes withautomatic, computational fact checking, the shortcomings of human factchecking are greatly overcome. With instantaneous or nearlyinstantaneous fact checking, viewers will not be confused as to whatinformation is being fact checked since the results are postedinstantaneously or nearly instantaneously versus when a fact check isperformed by humans and the results are posted minutes later. The rapidfact checking provides a significant advantage over past data analysisimplementations. Any of the steps described herein are able to beimplemented automatically. Any of the steps described herein are able tobe implemented in real-time or non-real-time.

Examples of Implementation Configurations:

Although the monitoring, processing, fact checking and indicating areable to occur on any device and in any configuration, these are somespecific examples of implementation configurations. Monitoring,processing, fact checking and providing all occur on a broadcaster'sdevices (or other emitters of information including, but not limited to,news stations, radio stations and newspapers). Monitoring, processingand fact checking occur on a broadcaster's devices, and providing occurson an end-user's device. Monitoring and processing occur on abroadcaster's devices, fact checking occurs on a broadcaster's devicesin conjunction with third-party devices, and providing occurs on anend-user's device. Monitoring occurs on a broadcaster's devices,processing and providing occur on an end-user's device, and factchecking occurs on third-party devices. Monitoring, processing, factchecking, and providing all occur on third-party devices. Monitoring,processing, fact checking, and providing all occur on an end-user'sdevice. Monitoring, processing and fact checking occur on a socialnetworking site's device, and providing occurs on an end-user's device.These are only some examples; other implementations are possible.Additionally, supplemental information is able to be monitored for,searched for, processed and/or provided using any of the implementationsdescribed herein.

Fact checking includes checking the factual accuracy and/or correctnessof information. The type of fact checking is able to be any form of factchecking such as checking historical correctness/accuracy, geographicalcorrectness/accuracy, mathematical correctness/accuracy, scientificcorrectness/accuracy, literary correctness/accuracy, objectivecorrectness/accuracy, subjective correctness/accuracy, and/or any othercorrectness/accuracy. Another way of viewing fact checking includesdetermining the correctness of a statement of objective reality or anassertion of objective reality. Yet another way of viewing fact checkingincludes determining whether a statement, segment or phrase is true orfalse.

Although some implementations and/or embodiments have been describedrelated to specific implementations and/or embodiments, and someaspects/elements/steps of some implementations and/or embodiments havebeen described related to specific implementations and/or embodiments,any of the aspects/elements/steps, implementations and/or embodimentsare applicable to other aspects/elements/steps, implementations and/orembodiments described herein.

The present invention has been described in terms of specificembodiments incorporating details to facilitate the understanding ofprinciples of construction and operation of the invention. Suchreference herein to specific embodiments and details thereof is notintended to limit the scope of the claims appended hereto. It will bereadily apparent to one skilled in the art that other variousmodifications may be made in the embodiment chosen for illustrationwithout departing from the spirit and scope of the invention as definedby the claims.

What is claimed is:
 1. A method comprising: receiving deliveryinformation for a package; monitoring the package using an autonomousvehicle, wherein the autonomous vehicle comprises a drone device; andtaking an action based on the package, including triggering anotification to a mobile device when the package moves more than athreshold amount, wherein taking the action includes moving theautonomous vehicle, wherein determining if the package moves more thanthe threshold amount is based on a depth map and comparing a depth ofthe package in successive images.
 2. The method of claim 1 whereinreceiving the delivery information for the package includes utilizingobject recognition with a camera of the drone device.
 3. The method ofclaim 1 further comprising monitoring a property with a security device,wherein the security device communicates with the autonomous vehicle. 4.The method of claim 1 wherein receiving the delivery information for thepackage includes monitoring and analyzing a network-based communicationto determine delivery of the package.
 5. The method of claim 1 whereinmonitoring the package includes automatically moving the drone device sothat the package is within a view of a camera of the drone device. 6.The method of claim 1 wherein taking the action includes triggering analarm of a security system when the package moves more than thethreshold amount.
 7. The method of claim 1 wherein the autonomousvehicle accesses “do not fly” zone information to avoid flying in a “donot fly” zone.
 8. The method of claim 1 wherein the autonomous vehicleis configured to stay within a zone.
 9. The method of claim 1 whereinreceiving the delivery information for the package includes detectingtriggering of a doorbell.
 10. The method of claim 1 wherein monitoringthe package includes detecting one or more objects within a specifiedrange, and if the one or more objects are recognized as unfamiliar, thenotification is triggered.
 11. The method of claim 1 further comprisingstoring the package with the autonomous vehicle.
 12. The method of claim1 further comprising avoiding flying the drone device in one or morespecified locations.
 13. The method of claim 1 further comprisingblurring out a portion of content acquired by the drone device.
 14. Themethod of claim 13 wherein the portion of content comprises personalinformation.
 15. The method of claim 1 further comprising implementing aprivacy policy.
 16. The method of claim 1 wherein the autonomous vehicleis configured to receive the drone device, wherein the drone device isconfigured to separate from the autonomous vehicle and fly separatelyfrom the autonomous vehicle.
 17. The method of claim 1 furthercomprising monitoring one or more properties with the drone device. 18.The method of claim 17 wherein the drone device utilizes a thermalsensor and/or a sound sensor for monitoring the one or more properties.19. A system comprising: a server; and an autonomous vehicle, whereinthe autonomous vehicle is configured to receive package deliveryinformation from the server and determine when a package moves more thana threshold amount based on a depth map and comparing a depth of thepackage in successive images, wherein the autonomous vehicle isconfigured to track the package.
 20. An aerial drone device comprising:a device body configured to receive a package; at least one propellercoupled to the device body; a plurality of information acquisitioncomponents coupled to the device body, wherein a first informationacquisition component of the plurality of information acquisitioncomponents is positioned in a first direction, and a second informationacquisition component of the plurality of information acquisitioncomponents is positioned in a second direction; a memory configured forstoring an application, the application configured to: process externalinformation, wherein the external information includes deliveryinformation for the package; generate a depth map using depthinformation; direct the aerial drone device to patrol an area based onthe processed external information and the depth map; acquire contentusing the plurality of information acquisition components; and determineif the package moves more than the threshold amount based on the depthmap and comparing a depth of the package in successive images; and aprocessor configured to process the application.